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I think I have to read the the piece that you have read it before, right before first time on it. So, okay. so we started this a second session about the gender equality management. And we have 4 presentations. And the first one is by Professor Muller, inequality and the amount of information contained in male and the female agenda

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is Professor Mueller connected?

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Yes, I guess I'm

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Georg Mueller: so I have to. I try to get to the screen.

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Yeah, now I can. I I I open the it on the the computer. Now let's try to to skip the slide. So if you can.

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Georg Mueller: I have a problem that there is a lot of text here up that can hide some part of my things here.

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It is a whole bar from you. I stop. I can. I try. Stop sharing. Okay, so we can see you now.

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Georg Mueller: Yes, I think it is your slides. Then

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Georg Mueller: those are my slides from my side that are not uploaded. So probably even.

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Georg Mueller: okay, okay, we can see your screen and your face. So we can see you your computer. And we can hear you. So it's part here which I now, everything is more or less. Okay, okay, so 59 min, please. Thank you.

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Georg Mueller: As indicated in the title of my paper. I'm going to talk about inequality and the amount of information that is contained in male and female gender

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Georg Mueller: the educational discrimination of women has to do with the role of women in family and society, and that changes over time. It changes by culture. So the goal of this investigation is to make comparisons of different cohorts and to compare different campus

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Georg Mueller: in order to tackle this goal. I would like to use entropy concepts for analyzing social inequality.

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Georg Mueller: One of the particularities of entropy concepts are that they are devil faced. On the one hand, they are in the inequality. On the other hand, it means information.

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Georg Mueller: So we will look at the relation between these 2 phases in the methodology section that follows, and subsequently we do some exemplary analysis with these indicators.

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Georg Mueller: that is, we look at the educational inequalities in Switzerland, Turkey, and Sweden, and we look at H. Cohorts between 1,930 and 1,979.

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Georg Mueller: Let me start with the methodology part the construction of indicators. As you know, there is a classical formula for entropy that was invented by Shannon and Weaver.

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Georg Mueller: Without going into the mathematical details. Entropy is the average number of binary decisions that have to be made in order to identify an element from a statistical distribution.

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Georg Mueller: So entropy in his own way is in equality with regard to privilege links.

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Georg Mueller: If entropy is the absolute minimum level 0. All actions have the same value. So there is minimal inequality. If the entropy is at maximum level, we must have our rectangular distribution of the privileges, and as a consequence, we have maximal in equality.

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Georg Mueller: as mentioned before, entropy can also be information about the social actual. I, if I am at the minimum level of entropy. 0.

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Georg Mueller: All actress of the same privilege

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Georg Mueller: at this means a minimal uncertainty about the privilege of a person which I do not know, but I know that there is minimum, and

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Georg Mueller: the maximum entropy means that they have a rectangular distribution. So there is a maximum of uncertainty about the value and the privileges of the person I'm talking about.

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Georg Mueller: The previously mentioned entropy definition can, of course, also be applied to sub groups. So I'm moving to conditional entropy where we are assuming that we know the membership

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Georg Mueller: of a person, let's say that the membership is in the group of men or the group of women. So what I have to do is simply to replace in the classical formula all 3

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Georg Mueller: probabilities by conditional probabilities.

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Georg Mueller: Important Socialist or sociological interpretation. Conditional entropy means the intra group inequality. We've seen, for instance, the group of men

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Georg Mueller: I'm now moving to. the another idea

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Georg Mueller: that is more oriented where the information part of entropy, and it is the information gain. If I know a group membership.

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Georg Mueller: then I can

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Georg Mueller: construct an information gain from this knowledge that the person belongs to the group of men, or belongs to a group of women, just by looking at the general entropy minus

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Georg Mueller: the interpret conditional entropy that results from the knowledge of being a man or a woman.

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Georg Mueller: So positive values mean that we have a reduction of uncertainty about the privileges just resulting from the knowledge that the person belongs to a certain group, let's say, of men or of women.

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Georg Mueller: At the same time. This, it means an increase of a information

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Georg Mueller: just due to the fact that we know the membership of the person.

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Georg Mueller: So there is a sociological interpretation.

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Georg Mueller: If we have positive information, things is in general That the property from which we are deriving this positive information gain is a social stigma.

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Georg Mueller: If it is negative, then the property, for instance, proof being a man is a social mask that heides some part of the reality.

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Georg Mueller: Another important concept is mutual entropy, or soon entropy.

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Georg Mueller: and without going into the details of a formula. Soon. Entropy is the mean information gains from knowing the membership in different groups.

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Georg Mueller: Sociological interpretation

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Georg Mueller: soon entropy is in a certain way structural inequality of chances. The information gains from knowing that the person is a man or a woman means that there is some structural inequality between the 2 gender groups.

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Georg Mueller: Finally. we can bring all these things together.

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Georg Mueller: and the mathematical relation that results here can formally be disused. This is the nice part of the story, so I do not want to go into the details. However, the total inequality with regard to a privilege is one

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Georg Mueller: the structural inequality or the in inequality of chances, plus the mean intra group. Inequality is out. Looking at this inequality of chances.

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Georg Mueller: I stop here. It's the mathematical part.

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Georg Mueller: and I'm like to go now to the inequalities with regard to education, eating men and women. I'm looking at my data set. That was the European value studies 2,008. And I am using here educational attainments, primary, secondary.

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Georg Mueller: and tertiary education. And of course, I need for this talk also gender information that is obviously available in this survey, and they look at different

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Georg Mueller: countries. I'm studying Switzerland, where I'm coming from

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Georg Mueller: target, which it was always a little bit delayed with our to send the questions, and Sweden, which is said to be advanced in 3.

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Georg Mueller: Send the questions

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Georg Mueller: empirical results. The different forms of

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Georg Mueller: educational inequality are now the topic to be discussed. Figure one shows the total inequality country and first code first courts you see here.

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Georg Mueller: from the horizontal axis between 1,935 and 1,975. And I have my 3 countries, Switzerland, right.

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Georg Mueller: Sweden, Blue, Turkey, Crime.

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Georg Mueller: Switzerland, and Sweden are reducing the total educational inequality over time.

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Georg Mueller: It means that we have here in this case a situation where more and more people are finishing schooling with touch education and the other 2 levels are less and less important. So there is an increase of

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Georg Mueller: educational, a decrease of educational inequality due to the fact that everyone nowadays has at university the

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Georg Mueller: quite to the contrary. the inequality with regard to education increases for Turkey over time

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Georg Mueller: dramatically, at the beginning everyone in Turkey had only primary schooling at the end. There is a mix between different levels of schooling, and this explains that at least temporarily, there is an increase of the total inequality.

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Georg Mueller: We might now in figure to look at the mean intra group inequality by country and first cohorts. So here we see the situation once again for Switzerland, for Sweden, and for Turkey.

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Georg Mueller: It is indeed the case that for Switzerland we have. and for Sweden we have slides the that means, for

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Georg Mueller: within the group of men and within the group of women that is less than inequality. And then we take the average

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Georg Mueller: quite to the counter rate.

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Georg Mueller: Turkey has an increase, and my explanations for all that will be that are the same. As for the previous figure one.

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Georg Mueller: Finally, figure 3. Structural inequality, discrimination, so to speak, by Kandra and Perco work

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Georg Mueller: there is no clear trend at all, and there is also, no stable ranking of the countries. However, what strikes is the fact that on the average we have here a value of point 0

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Georg Mueller: for this record to discrimination.

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Georg Mueller: And please compare that with the previous slides the intra group inequality.

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Georg Mueller: Here you have a value of one so discrimination is here in this case

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Georg Mueller: of education, rather small, and I think it is not so unexpected. In a certain way education is in all these countries a universalistic promise, so

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Georg Mueller: too much discrimination should not occur.

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Georg Mueller: Empirical results. Part 2. I told you that there are information games from gender with regard to education.

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Georg Mueller: So first, what do we learn from the fact that the person in front of us is a woman.

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Georg Mueller: and how much information can we extract and gain for that?

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Georg Mueller: Look at this 0 line, and then you see that there is for women practically always a positive information games.

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Georg Mueller: So female gender is, in a certain way a stigma, and it points in most cases not all, but in most cases do a lack of higher education for women.

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Georg Mueller: This interpretation is especially true for traditional Turkey. To see the gray curve represents Turkey, and the information gains here are dramatically high.

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Georg Mueller: We can do the same exercise information gain for

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Georg Mueller: men.

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Georg Mueller: We are looking at the information gain that can be deduced from the fact that the person in front of us is a man.

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Georg Mueller: Here we see for many cases, negative values and negative information. Kings mean that may gender is a social mask in every moment when the person in front of us

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Georg Mueller: this place to be a man, we do not really know how much information how much education the person really has.

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Georg Mueller: However.

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Georg Mueller: that is a special case. Blue

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Georg Mueller: Sweden. Here we always see that men have values of information came greater than 0. What you need to know in updation is that Sweden had

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Georg Mueller: for a long time since 1,935,

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Georg Mueller: an educational system where women had more education that men. So in a certain way, in the case of Sweden it is the male gender that is the real stigma.

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Georg Mueller: pointing to a lack of education as compared to women.

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Thank you for listening.

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Thank you. Thank you very much. Any questions from the floor.

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Yes, okay, it's please, Sandra.

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Oh, thank you, thank you so much for your

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clear, clear, clear explanation. But even though I would like to ask you about the structural A in index. If you can say something a little more about this, not a clear trend, why it does mean, or

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to what element could be involving in this not clear trend? Thank you so much. Your your explanation was really interesting.

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Thank you.

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Georg Mueller: I do not have an explanation. Why, it's varies so much. Perhaps it has to do with the fact that the effects of structural inequality are relatively small.

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Georg Mueller: this is the best explanation which I have. Why, we do not see systematic patterns. Sometimes it's more than it is less, and so on.

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Georg Mueller: So the message is that the inequality within the group of women and the inequality within the group of men is much dramatically much higher than if we are looking at the discrimination of to gender groups with regard to education.

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Georg Mueller: so

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Georg Mueller: I can not give you a clear answer. It's indeed astonishing that is structural inequality. Does not this play more a clear pattern than it is, of course you can average, for instance, the valley of Switzerland. Then, if you start in 1,935,

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Georg Mueller: the curve slightly decreases that it lead to 0. for Turkey it is more or less constant for Sweden. It will be you shaped, but I lack the precise knowledge of what has happened, at least in the all the 2 countries which I'm not so familiar with.

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These are here.

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maybe for the change of a question. Maybe the cohort change the marginal in the table of the contingency table.

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Maybe the structural change is related with demographic demographic changes, and maybe for that it is more difficult to understand, because we have a different cohort

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and and maybe and then the educational expansion is too fast in this

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cohort. And then there are a lot of changes there, and maybe the 2 things that is a demographic and also the educational expansion

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are they are doing some things that we cannot see very clearly. But it's really interesting to think about that, because you show the the complete picture men, woman, education and cohort. And and it's a really really interesting. Thank you.

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Georg Mueller: I think the idea of looking at demographic processes is really brilliant, and I think, in terms of immigration. You know, Switzerland has always been an immigration context, and one would have to look at these changes from the fact that from

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Georg Mueller: Southern Europe

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Georg Mueller: there was a lot of immigration of not so much qualified and not so well educated people from Sicily, from south of Italy, and so on, and perhaps these influences in a certain way, things, at least for Switzerland in a very similar way. You could also interpret the situation with C. Regard Turkey. Turk is a classical immigration country.

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Georg Mueller: and those who are qualified and well educated leave Turkey

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Georg Mueller: and

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Georg Mueller: let other people behind. Perhaps this is indeed something that should be considered when interbreeding these things here.

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Okay.

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other questions from the floor.

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Otherwise I have 1, 1, 2. So if understood correctly. with the Entropy Index, you consider data that are not ordinable or or not ordered.

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So I was. guessing what the are the education levels that you have a considerate in for the analysis. Or maybe I missed it, the primary saying, or it that's really. And if these educational attainment that the ordering of this is somewhat considerate in your analysis, I mean,

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I expect the people moving, or, for example, targeting, moving more from primary than to secondary, and finally to tertiary education. So the the progress of the educational level of a country may be interesting to be to be included. So I don't know how this ordering is included in the in the analysis.

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Georg Mueller: No, the 0 ordering is not really included in the analysis.

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Georg Mueller: and one would have to think more precisely about how to introduce this in. The idea of entropy, however, entropy is in a certain way blind to any ordering of the privileges.

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Okay? And that also another consideration about darky. But was the interesting. Do you think of the fact that the entropy is increasing in Turkey? denoting a higher in a quality level is, however, connected with a an average higher educational level.

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I mean, in general, there is a progress towards a higher educational levels of some groups of populations, so the progress in of educational attainment may be associated at the beginning to an increase of heterogeneity.

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and maybe in the future, with future generations we could see a decrease of heterogeneity when the lower educational levels will reach a higher educational level. I don't know if this can be an interpretation.

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Georg Mueller: It will indeed be an interpretation. Please sync because of, let's say, Switzerland, together with Turkey, and then you have first of all an increase, and after some time a decrease. So it is a inverse U-shaped curve that will turn results. So what would be interest? This to look at

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Georg Mueller: the real country that made this kind of progress

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Georg Mueller: from a primarily educated country to a tertiary educated country, and then we should indeed discover this kind of curve which you are mentioning in your argument. I do not know

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Georg Mueller: which country would be a good case in order to show really this kind of process it might be. You have an idea.

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Georg Mueller: No.

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no, we don't know.

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you know. Then maybe maybe also Italy. I don't know.

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Even if it is, maybe

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perhaps okay.

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And any other questions from the

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the floor. No, okay, so thank you very much. We have the last question very last minute question.

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could you explain why you chose entropy as a way to to analyze this data. What is the argument for this? For this method of analysis?

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Georg Mueller: The best answer to that is that we can use entropy in several ways. On the one hand, it has to do with

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Georg Mueller: information, and, on the other hand, it is also inequality.

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Georg Mueller: So I wanted to bring these 2 concepts together

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to that.

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Okay, so thank you very much. And the presentation was really appreciated. So thank you. And now we move to the second the presentation. Thank you.

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Collapse from the room.

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sorry, just to some. If there are some questions on the chat.

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okay, Anna Kurovska. But now I think you you hear? Okay?

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Then we have a second the presentation by Hewa Dayton, who sent to me an email that he's not. He's here in Melbourne, but he got the flu. So I'm sorry. I hope you feel better. Now.

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Heiwa Date: Yeah, can you hear me?

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Yeah, yeah, we can hear you. Yeah, yeah, yeah, okay, thank you very much. Okay. please share your screen.

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Heiwa Date: Yes.

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 okay.

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Okay. Thank you.

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I also for you. 15 min from now. Thank you.

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Heiwa Date: Okay.

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Heiwa Date: Halla Devon, I'm here with that there from Shega University, Japan. At first let me approach us for not to be with you. I mean, now I'm able, but cannot go up

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Heiwa Date: out the room because I catch a call this. So my.

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Heiwa Date: this yeah, I think I should not be with you so sorry there! Sometimes it is difficult to participate to hear my voice, so I try to speak as a clearly as possible.

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Heiwa Date: I'm interested in micro level. Obviously, it's such as individual altitude. I'm sorry that it is not a kind of social indicator. But yeah, I want to present this. I'm engaged in international comparison studies about family, especially on the agents, Asian societies.

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and mostly I focus on general attitudes in Asia

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Heiwa Date: then, but there are some problems in comparing countries equivalent to this, because this is because of the tendency to choose intermediate answer of Japanese people.

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Heiwa Date: So let's begin this video.

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Heiwa Date: I'd like to start with this feature. This figure shows the distribution of gender load our shoes

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Heiwa Date: little out of shoes under this blue means

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Heiwa Date: Oh, sorry at the item. It's saying a husband's job is to a money. I want to always to look after the home unhappy. So this is a famous item all related to general attitude. Some blue means disagree, so blue is liberal, and the let is

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Heiwa Date: conservative. So this is I. I agree with this as they are all the issues, then this

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Heiwa Date: this block means, neither agree no disagree. So middle answer.

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Heiwa Date: So as you can see, the Japanese holly tended to choose intermediate answer.

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Heiwa Date: So sometimes it is difficult to interpret. So in the case we focused on, let colors oh, oh, this is Hanoi and Hanoi different countries. Oh, this is conservative compared to Japan. But if we focus on blue line a blue

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Heiwa Date: oh, it

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Heiwa Date: Japan Japanese, Liberal or not. I'm not sure of something like that. Well, I don't. I didn't show the mean score. If we cultured mean score Japanese looks like a little bit out

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Heiwa Date: because they say they tend to choose intermediate answer something like that.

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Heiwa Date: It's the situation. So

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Heiwa Date: so I, when I

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Heiwa Date: participate in the International Conference participants always asking me, Why do you mean Japanese? Choose Nazi agreement? They're they're they think, when they choose this or send deciding major gender, or I choose property or something like that.

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Heiwa Date: So I always

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Heiwa Date: always as well. It depends on it's because Japanese or something that I know. This is the answer. But

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Heiwa Date: this is a situation. So

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so

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Heiwa Date: I want to. I want to ask this question this time.

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Heiwa Date: So in the text book of social service this thing thing, how to deal with the int intimate intermediate answer in a social subway

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Heiwa Date: there there are various viewpoints, so some sociologists said, no clear answer, or should use that should not use. It depends. It cannot be generalized. Or if you could, both should be used.

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Heiwa Date: it's not possible. But yeah.

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Heiwa Date: and

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Heiwa Date:  So their problem is caused by using a middle answer.

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Heiwa Date: So the results are not clear, because many people want to choose middle answer, or we cannot know that people people truly express an intimate position if they choose this.

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Heiwa Date: or some people say there are problems with problems caused by not using a middle answer.

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Heiwa Date: So there are risks

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Heiwa Date: all. No, this is a drop out due to highly burden of respondents or measurement of errors. For example, people who have different popinos may choose the same option like a somewhat decisive, or some of this

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Heiwa Date: somewhat agree, or something like that.

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Heiwa Date: And this and last negative, important impact. You got to be important here. The impact on the missing mechanism. So if people with certain attributes tend to choose, don't know, then there is a problem with missing values, not Oakland group occurring Londonbury.

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Heiwa Date: So this problem needs to be solved in terms of social research method.

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Heiwa Date: And there are 4 main interpretation of a middle, also of researchers.

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Heiwa Date: and first of all, to an intermediate position statement, it is desirable

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Heiwa Date: interpretation, like a neutral position, need a full no against

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Heiwa Date: There are times when I agree, and times when I disagree, or it cannot be determined as it. It depends on conditions something like this.

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Heiwa Date: a second, and in capability, or making a judgment and lack of interest

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Heiwa Date:  in the content or lack of opinion, or that capability.

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Heiwa Date: And this is a similar to second point, I avoiding burdens, and finally, social disability by us.

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Heiwa Date: so so the disability bias obscure. So the attitudes of those who hold negative opinions about Jenni colleague.  they, In other words, those who hold a negative opinions about the genetic quality don't want to show

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Heiwa Date: their opinions because they clearly understand this just society is going to be gender called society so. But these interpretations are conducted by researchers. So we don't know how we responded. Think

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Heiwa Date: so, Sturgis. I purpose, according to paper studies, searches in turn. Teams salvate the reason for selecting the middle answer, to ask the reasons so to choose a middle opinion. So

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Heiwa Date: no, they have 3 choices. I don't really have an opinion on this issue. I have an opinion which is right in the middle on the issues, and 3 needs a out of up, so say, conducted additional interview.

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Heiwa Date: So the results is here, most of the respondents answered. I don't really have an opinion on the issue

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Heiwa Date: then. if they interviewed most of the interviews, explain, I don't know. So this is

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Heiwa Date: This previously suggested that very few respondents hold neutral position. So they are

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Heiwa Date: almost of all hidden. Dk, so Sturgis, at our Fs as a meaning of hidden Dj in the middle, answered on the middle answered, but it is obvious also in Japan. I think so. This is such a question. When Japanese who selected the need that are in a disagree. I asked, why you did so, how to explain this?

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Okay. this is a

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Heiwa Date: how to collect the data top and missile this interface. It's it's have our 2 s. 2 sons with 23 to march.

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Heiwa Date: A target of operation was a people living in a pen, as you 2,096. This temporary method is quarter sampling, stratified by area

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Heiwa Date: 8 area and gender, and each group. So we can get us 160 sales, so it in itself to sample size what was allocated, so that the prop proportions would be the same as in the census conducted in 2,012,

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Heiwa Date: 20 to some 20. So it's this is a a latest, and census in Japan Some size number is 3

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Heiwa Date:  emphasizes 3 styles on the side of fold, and the labor segment is omitted.

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Heiwa Date: And this is the question here. Now the contents of question here 6 I. there are 60 items of that, all attitudes, and for up question reason for this choice, and how they change the opinion if asked on a 4 point to scale, plus don't know.

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Heiwa Date: And and

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Heiwa Date: the other viable is of like like this as follows, education level for existing household chores, it's not for spouse on the working status, working ours, income, children, and so on.

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Heiwa Date: And this is a procedure procedure.

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Heiwa Date: And for all, what do you think of each of following opinions. Please choose the answer closest to yours. The men should earn the living, and the woman should take you over the home.

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Heiwa Date: and a more 15 times 15 items, and they select, agree

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Heiwa Date: somewhat agree is agree. No disagree, somewhat disagree. They disagree that it's 6. Don't know.

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Heiwa Date: Then to this.

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Heiwa Date: I can. They should earn the living, and woman should take your home.

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Heiwa Date: Take take care of the home

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Heiwa Date: as for men should.

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Heiwa Date: and the living and the woman should take you up for the home. Why did you answer like this? Please tell us the reason for your answers as long as possible. As precise. So this is a free answer.

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Heiwa Date: Yeah. Using work.

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Heiwa Date: then, for people to to choose need a green. I disagree. If they are not need, they are going to this? I really, how do you answer from

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Heiwa Date: this item? So please let me call this this variable

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Heiwa Date: to go for point to scale. So I agree somewhat that we some of this agree and disagree and don't know. So there is no need to answer.

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Heiwa Date: This is a results

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Heiwa Date: the first. It just shows the results of 5 on the scale and don't know. And fortunate trio of a and 31.2% selected in need. The agreement disagree in this

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Heiwa Date: sample

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Heiwa Date: I don't know. It's very few, 1.7%. So then here is a siddle for a pseudo for point of scale.

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Heiwa Date: So agree and disagree. It's about half and a half.

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Heiwa Date: and decay is also few in this sample

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Heiwa Date: so, and it's just as As for charts, that is so, 5 point scale into the middle answer. If we permit the middle answer to a number of people who choose don't know would be few

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Heiwa Date: instead. May may many people choose a middle answer, as you see. I mean, that's for charge there still, all the pseudo from point to scale, excluding a middle answer. The number of people choosing don't know is unlimited, so this means that

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Heiwa Date: not the many people fit the hidden decay in the middle answered, and contrary to the previous research, and the people choose either agreeing or disagree can decide their attitudes. However, the list of those who agree on those who disagree is half so. It is impossible to determine whether the person who choose the middle answer is conservative or liberal.

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Heiwa Date: and this is a results of content analysis. and I classified reasons into 8 categories so category and a dk. As for people who

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Heiwa Date: did not answer or said, I don't know nothing special. This is a Saturday 8 person.

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Heiwa Date: and the category about giving the clear answer is for people to leave the decision to others without giving personal reasons is a 30%

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Heiwa Date: to see the category alternative tradition gender low is for people offered an alternative to the tradition division of gender load is about a 15%

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Heiwa Date:  then tategory sympathy for egaliat in zoom. And it's people show sympathy for the icon values as as a reason. It's and the number is limited

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Heiwa Date: a dilemma between Polytown and loyalties. People express a dilemma between the ideal general equality, and it's all the it's that it's not. And this is. This is the or general old People refer to social situation where the traditional to be some labor road is necessary.

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Heiwa Date: and as far as the sympathy for traditionalism. people show a sympathy for conversative values as the reasons.

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Heiwa Date: So this is a examples like, avoiding giving clear answers, there's a lot of different the circumstances for different families, or it depends, and mothers are better off taking care of their families before the children at the

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Heiwa Date: or because it is unappropriate to speak in general terms as it varies, depending on factors such as family instructional income, something like that, so they can be interpreted as trying to represent the neutral position by avoiding clear, clear opinion.

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Heiwa Date: this is alternative gender laws, such as for example, the woman want to work, and the woman should work too, or I want to meant to participate in household chores depending on the situation and the character of the family. The laws could be leversed.

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Heiwa Date: or blah blah blah! I's so tempted to meet this, so I will skip this point, but they can be interpreted as a hidden liberal.

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Heiwa Date: Now this is a empathy for a gratinism like I can say either way because of gender quality, or because I don't think it's a good idea to decide what you're going to do because you are now because you are a man.

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Heiwa Date: This is a I call

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Heiwa Date: I think this is a so to disability by yes, it's it's the of the gender tips of labor.

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there are so many obstacles for zoom to work. So I think that there is a income gap between men and women.

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Heiwa Date: Mothers are needed. You are in childcare, so it is difficult for men to repress this role, or something like that.

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Heiwa Date: And this figures shows a close taberation on content. Sound of pseudo, 4 point of scale. So people who cannot explain their reasons didn't choose. Dk. It is an interesting point

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Heiwa Date: on the pseudo. 4 point of scale, so say, say.

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Heiwa Date: want to express their opinions if they cannot explain.

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and the people to avoid the

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Heiwa Date: position of that People who avoid the position, opinion.

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Heiwa Date: and the explain alternative general shows various various position.

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Heiwa Date: People who have the

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Heiwa Date: and the people who emphasize the necessity of general art. Gender, law and sympathy for traditionalism show traditional attitudes.

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Heiwa Date: This is so

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Heiwa Date: times over some I this I skip this summary. So

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Heiwa Date: this is a conclusion.

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Heiwa Date: So now I I'm asked, why do you man Japanese choose? Miz? Agree, not disagreed. I just said, it is. Still, it depends on the situation.

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Heiwa Date: But an I can say more clearly on a 35.1 is the hidden Dk as 70 plus about 30%, it immediate position.

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Heiwa Date: and the 20 hidden label and the

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about 7 hidden conservative. At which scale should we use? As for fine for the scale. It's easier to analyze because it is, although a larger, valid sample size due to fewer decade. On the other hand, it should be noted that neither agreeing or disagree includes a variety of positions.

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If he compares the societies and use the 5 point scale, we should focus on the participant age of only algorithms. Somebody agree or disagree with some of the so-called equivalent analysis.

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Heiwa Date: They are folk on the scale. One guess, interpretation, impression. One is a impression that there is a clear split between agree to disagree on a 4 point scale, which is often used by the server government, but it should be noted that the 4 point to scale may inherently includes people from various positions, especially when I say, Choose somewhat agree with disagree.

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Heiwa Date: So this is all my presentation. Thank you for listening.

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You

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thank you. Any questions from the floor.

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You have the gotten. No.

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okay. I have done a couple of questions with nobody else. Okay, the first question that I would like to to ask you is this one? Sometimes, when we have a lot of intermediate points in in a liquor to scale. For example, it is because the respondent was not

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sufficiently informed, or there were not 2 questions before that the opened the the road for building an idea, because sometimes answering it to a questionnaire. It is not easy. You you don't, to exactly know what what are the questions. So you reach a question if it is critical, and you did not have the time to to arrive at that point.

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Do you think that that this could be also the reason why, there are so many intermediate answers, I mean, the the positioning of the Quebec of a question inside. The sequence of questions of the questioners may be relevant for for this.

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And and then I have another point about. I will ask later.

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Heiwa Date: Okay, I'm not a good English speaker. So could you summarize again?

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Yeah. So now, in that, in many cases a question and

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pose the questions in a specific order.

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such a way that the the respondent of a person who is responding to the questions can make up his, her mind on a specific topic.

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If the question arrives too early in the questioner, it may happen that it, the the respondent, did not have a clear idea of a problem, and this so it does not have a clear answer to give them. So neither the agree nor disagree may be. Sometimes it can be due to the fact that the respondent did not make up an idea clearly.

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Heiwa Date: did you understand the better now what? What I said so in general. So so sometimes, yeah. So, as you said, it's difficult for us

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Heiwa Date: to to to be asked to

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Heiwa Date: what I

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Heiwa Date: didn't didn't didn't know the situation. I didn't know the word or  yes, we. We. We have to choose like need agreed, and disagree.

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Heiwa Date: But in this, in this study, so

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Heiwa Date: it is. It is I I use more

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Heiwa Date: understandable question about the general attitude. So maybe

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Heiwa Date: I'm not sure but yeah,

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Heiwa Date: Sorry about

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how can I? You have it to think a bit about it. Okay, no, no, no, no, no, no, maybe it does not make sense my question. But it was just an observation.

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Yes, we have a question from the floor. Just a second, because she has to come in front of the microphone.

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Hello, thank you very much for your very good presentation. Maybe it's not the question, but the suggestion. After your question. There's some technique. it's called a split ballot technique, and we can use the

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yeah. So you can also check if there will be some diversions in between.

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these 2 groups, if the group has a different

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different type of questions in different order of the questions. Yeah, thank you.

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Heiwa Date: Thank you very much. I will ask

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study about the

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so basically, if I can synthesize. The idea is that to to try to make the questioner so that the respondent can make a a personal idea or can develop an idea on the topping while responding to previous questions. So, little by little, he she is brought on that topic. And then you ask.

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you could try, for example, 2 different questioners, the version that you already did, and in our version, and to see if the number of intermediate questions, the answers is lower. If you give people with time to

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made up of their mind on on the topic. This is the the idea that they came from the floor.

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There is a sorry.

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Okay, Ilala, please open your microphone. Thank you.

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Hilal Arslan: Okay. Can you hear me?

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Yes, yes, we can hear you. Okay. Great. Thank you for the presentation.

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Hilal Arslan: I have a question regarding related with the background of the participants of the survey, like they do

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Hilal Arslan: or is educated at education level is related to the answers for the needers or more category, that because you already collected some social demographic variables for the participants. And I'm also curious about like the age and education, and both the intersection of it.

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Hilal Arslan: and also the gender in these like. What amazing moments! If you check it. Thanks. Thanks a lot.

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Heiwa Date: Thank you for your comments that what you mentioned is actually what I what I do in the future. So because you do, and I have collected a more demographic as variable. So I should check the real relationship between the disaster and and the demographic fire force. Thank you.

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Yes. Another question from the floor. Please.

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Thank you very much for very interesting and practical presentations. So the question is, do you think that the high rate of our intermediate answers is specific. in Japanese culture for gender equality? questions. Or is it general cultural things that

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in Japan people try to avoid very direct opinions or clear opinions.

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Heiwa Date: Yeah, I think this is a

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Heiwa Date: yeah. You know, Japanese are tend not to say Korea, because we we want to. we are living in our Co collective culture. So it's like that. So we we care about the families. So societies of friends, or so many

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Heiwa Date: I so many

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Heiwa Date: p people. We here, we we we don't have to hear about, we we sometimes

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Heiwa Date: saying, have to care something like that. So we we think so, we think various cases

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Heiwa Date:  about and send a load.

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Heiwa Date: So in domestic kind of

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Heiwa Date: domestic part or social part. So so that's why jeopardy is sometimes confusing when when we face

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Heiwa Date: that such kind of question.

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Okay, thank you. Other questions. No, okay, so thank you very much.

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Thank you very much.

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Now, a small advertising for at a C. 55 is a 2 sessions this morning are organized by our C. 55, which is the group of social indicators. So if you are not the member, so we invite you to to subscribe to our group. because, as you can see, we are. don't. If you are interested on the topics of a gender equality measurement, of course, because the under quality is a wide topic, but about the to measurement.

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We are recruiting now we want you, and we are mostly welcome, for if you want to join.

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so now we have the last presentation, because the presentation by Leon Friday is recorded, so we do not display it right?

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So we we don't have to display a recorded the interview, are we okay? Okay? No, no. It is a recorded presentation. Yeah, yeah.

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yeah, yeah, exactly.

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It's better, then to go with the last presentation of a program by Hassan, a Kan from the Turkish Sociological Association.

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The University.

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I see.

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Hilal Arslan: Hi! I'm a co-chair of the Hassan. My son is here, and I'm registered to the conference, and I thought that we I can present the papers.

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Hilal Arslan: But luckily he is also here. So if it's okay, then I will present. I'm going to present the paper. Okay? No problem, no problem. Absolutely.

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Hilal Arslan: May I share my, yes, please. Okay, great.

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Hilal Arslan:  give me a minutes.

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Hilal Arslan: Sorry for the delay.

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No, no worries. Take your time. You're not in a hurry.

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Hilal Arslan: Maybe I will just

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share the screen and that okay, it is a sharing. Now, okay, we see your screen.

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Hilal Arslan: Yeah.

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Okay, thank you. 15 min from now.

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Hilal Arslan: Okay, thank you very much. And thank you for all the organizers for this session. It's an exciting for us, because Hassan and me, we are working on a project like the gender call. It's monitoring gender equality in Turkey to strengthen the participant or democracy

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Hilal Arslan: which is the EU delegation funded project and based on this project, we were working on the gender data on gender gender indicators

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Hilal Arslan: So our presentation is about like gender data for empowerment, and also as a measure, alternate right based measure for gender equality we will introduce A. J. It's a gender equality index.

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Hilal Arslan: that. We do a lot through the project just like I I would like to begin with that, Backgrounds like Turkey as a country that we also witness like on the other countries in the world. The gender backlash seriously, with the also rise of the nearly burial or Tartarian government

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Hilal Arslan: a. A. Especially in the last 20 years, and then, like. There are some highlights like withdraw from the Istanbul convention. That's the European Council

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Hilal Arslan: Convention on the like. combating violence against woman, and also, like many woman oriented policies, we witnessed it. turn this on back to the family oriented policies, and that. This is a period also, like using the term gender equality is banned from all official documents, and instead of this.

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Hilal Arslan: like, there is a more man and movement equal opportunity, as a term is preferred. But gendericult is a kind of

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Hilal Arslan: for dangerous thing to talk about it. And lastly, in the last month, in the elections, through through during the elections in Turkey. We really like observing that the Lgbti plus as a as a like a terrorist group, which is.

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Hilal Arslan: I label, heavily labeled and discussed by Populist and and conservative political parties. So that's a quick background like this is in this context. We

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Hilal Arslan: when I like to discuss the gender equality, monitoring that dark-based gender recall to monitoring with gender indicates to gender indicators, and by using the gender data. And and also we would like to share the knowledge and experiences in order to shed light on that right-based method or legit to develop gender equality in indicators, and by using cross-nationally comparable gender equality index.

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Hilal Arslan: And we like all materials and all data that we produce. we share with the activists and politicians and any Academicians, where the gender data portal that we developed throughout the projects. So

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Hilal Arslan: and theoretical framework for this study. Like, first of all, we, we have an approach to right based approach to monitor the gender equality. So we start with the all basic like to human rights and also the importance.

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Hilal Arslan: conventions like Sidel and also a paying, a paying commitment. And this, the action plans and these are the all like Lonzoretic convention. These are all international convention that we take, and

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Hilal Arslan: as a base to develop our January call would say, indicators, and also, like the J right base EU generic call to monitoring requires that independence, monitoring mechanisms so like the body doing the monitoring should have a distance to States and State institutions.

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Hilal Arslan: And also we address like the gender data? Yes, and all like in national statistics office. And what what type of gender data is now produced by officially, and what should be the gender equality indicators should be the data for the gender. Equality indicators should be provided

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Hilal Arslan: in many different thematic fields of the gender equality from sports to justice, or from Ss to like religious services, or an urban urban rights. So in many different fields, we develop the gender indicators.

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Hilal Arslan: And so based on this like, based on this approach.

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Hilal Arslan: I will introduce you basically likely also developed a gender equality index in this this index is.

361
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Hilal Arslan: as in in methodology, is a similar to a index developed by a European institute of Gender equality aia index

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Hilal Arslan: We have, like in the 6 domains the one of them the satellite domain. Right to leave without violence. we have in total 32 indicators.

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Hilal Arslan: the main domain sub domains of our E. Gender. Equality index is the right to better life, right to education, right to healthy life, right to work, right to participation in decision making and and and right to leave without violence.

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Hilal Arslan: And these are all the way to averages the calculators for each indicators and the index text values between 0 and one

365
01:01:48.810 --> 01:02:00.709
Hilal Arslan: based on the gender gap metrics like the higher inequality, the closer it's to 0 and lowering equal to the closer to the one

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Hilal Arslan:  so also

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Hilal Arslan: choose our indicators. Like nearly in. I can say that in 16 areas, the key areas of gender equality

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Hilal Arslan: the export working group on indicators. they will of some like indicators for monitoring. So we select some basic indicators on the curriculum of compliance with international organizations, definitions, and revisions of the indicators, compliance with definitions and expression in national indicators and scope of indicators like whether they are produced at the level of statistical regions

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Hilal Arslan: and available to by years. And it's like the continuity of the data and existing and alternative data sources.

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Hilal Arslan: so in in the domain of right to healthy life. We have a indicators like life expectancy by gender proportion of woman who meet their family planning is with modern methods more than contraceptive methods, and the satisfaction with general health status by sex and adolescent maternity rates, and in right to education.

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Hilal Arslan: And we have average years of education. by sex proportion of high school graduates, percent of, you know, university graduates and a percentage of those who didn't complete any school.

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Hilal Arslan: And in the size that you've seen every score of needed reading skills. We use this to indicate this from the piece of survey, and also the mathematical literacy. And lastly, the needs, like proportion of young population, who are neither in education or nor in employment, is also included in rights to education. So to me, in right to work. We included the right ratio of employed people.

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Hilal Arslan: and that's on enrollment rates in preschool education by sex. And this is important to understand. Like like women's participation into labor market

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Hilal Arslan: and the percent show people employed by part time and unionization rights, by sex and proportion of beneficiaries, of collective bargaining agreements by sex and proportion of young population who are, and it was, and proportion of the unemployed by a gender. These are the indicators related with the employment.

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Hilal Arslan: and we have the right to go, then addict to son out of flowing in this domain we use the Gdp for capita. I need to use rates of individuals by gender and paid work, time and unpaid work, time

376
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Hilal Arslan: and

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01:04:56.470 --> 01:05:08.749
Hilal Arslan: the last last main domain right to participation in decision making. We use the indicators. Proportion of company executives who can trade on Boris, Istanbul.

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01:05:08.810 --> 01:05:17.779
Hilal Arslan: and proportion of employees in managerial positions by sex and proportion of those in senior and middle managed

379
01:05:17.780 --> 01:05:40.659
Hilal Arslan: management positions and the representatives. This is this, an indicator, as some are indicator for political participation, like proportion of representatives elected to the national parliament and proportion of city Council members. This is the elected presentation of the general in in local government, local administration.

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01:05:40.660 --> 01:05:45.680
Hilal Arslan: and proportion of judges. This is the last indicator

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01:05:45.850 --> 01:05:55.309
Hilal Arslan: and right to leave without violence. we used to. physical and sexual violence. The

382
01:05:55.320 --> 01:06:06.469
Hilal Arslan: yeah. Prevalence of the physical and sexual violence among the women, aged between 15 and 59,

383
01:06:06.470 --> 01:06:27.059
Hilal Arslan: and also we edit the like child in early enforced it marriages as an indicators like proportional from on age between 20 and 20, or who who got married before the age of 18, and the rates of individuals with feeling at safe at night in their living environment.

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01:06:27.060 --> 01:06:35.869
Hilal Arslan: also the last indicators for right to leave it out while us so I can show you like

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01:06:35.970 --> 01:06:41.000
Hilal Arslan:  this is the total scores

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01:06:41.140 --> 01:06:59.860
Hilal Arslan:  here in our index. When we compare with the Oecd countries, we the Sweden has a score and second one is the New Zealand And the third one is the Australia as a country that's who has

387
01:06:59.860 --> 01:07:13.899
Hilal Arslan: and the who have, the higher performance of gender equality. And in among the 26 countries in Southern Sardon Kore is the last one, and to 25 fifth countries the turkey

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01:07:13.980 --> 01:07:39.420
Hilal Arslan: and the Japan is takes the score of 24. So this is the last 3 lowest scores of gender equality. So you in his. In this graph you can see the like. The the sub domains that the countries got the scores for the gender equality index. And so I can

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01:07:39.500 --> 01:08:06.650
Hilal Arslan: say that in Turkey the right to participation in decision making, not only in Turkey and in many, many countries as well. This is the lowest domain that the the the the country's platform lost. But education and in Turkey and hats right to have to. Life is the better one sorry

390
01:08:06.650 --> 01:08:14.890
Hilal Arslan: and I would like to also the share with you the the the data ports, all that

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01:08:15.180 --> 01:08:16.669
we do a lot

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Hilal Arslan: that you can like. have a chance. Sorry for that. You can have a chance to.

393
01:08:24.570 --> 01:08:25.870
Hilal Arslan: I.

394
01:08:26.970 --> 01:08:56.949
Hilal Arslan: So this is the data portal. Sorry that it takes a little time, but you can have it. Have a chance to observe all the indicators that we develop in in 16 timetable fields of gender equality, like A from starts with the aging and employment in education, access to high services, and also the the indexes in the in nexus that we also the developed.

395
01:08:56.950 --> 01:09:06.320
Hilal Arslan: So in this we continue our study. to compare the

396
01:09:07.150 --> 01:09:23.300
Hilal Arslan: our indicator. Right? Based indicators to others. International indicators. and also by by working with the local, Monetary equality platforms.

397
01:09:23.300 --> 01:09:41.870
Hilal Arslan: We are also trying to do a lot, some local indicators to understand, like the regional differences in in terms of the gender equality. So this is the end of my presentation, and thank you for listening to our

398
01:09:44.910 --> 01:09:46.980
questions from the floor.

399
01:09:47.010 --> 01:09:50.550
Yeah, Sandrafa Kelly, please.

400
01:09:54.660 --> 01:10:09.960
thank you so much. We have a very, very similar presentation this morning in the first session, because we present the

401
01:10:11.310 --> 01:10:17.399
okay? Yeah. to to them

402
01:10:18.450 --> 01:10:31.299
to see all the indicators from the attitudes European Institute with all the countries, and I understand that you have done and apply

403
01:10:31.300 --> 01:10:56.230
this indicator, the general indicator of Europe to Turkey, because you have not in this group of that is a very, very important task, because we have done this, and we know very well the important effort that this deserve and really thank you for that.

404
01:10:56.400 --> 01:11:15.790
my question is address. today to ask you about the violence indicator, because, as you know, in Europe still are working on that, and I can notice that you include some indicator of of violence, one.

405
01:11:15.790 --> 01:11:42.119
And then, if if it is available in your country or that, or that, if you can explain a little all, and also mention the overall index, which was the number for the year that you are calculating in order to compare with around 68 of the medium of European media.

406
01:11:42.260 --> 01:11:43.100
Thank you.

407
01:11:45.280 --> 01:11:59.850
Hilal Arslan: And thank you. Thank you very much. for the question. indeed, you're right. That's the violence against woman, is there? I think the the domain that we have a problem with the data access.

408
01:11:59.850 --> 01:12:28.019
Hilal Arslan: and we use the latest violence, national violence against woman survey, which is conducted by Institute of Population studies that I also work and take part in it. And it's so the the the data, the latest data available is 2,014. Indeed, we will Now the need wave of the survey, and we will conduct it in 2,014. The national violence against Women's Survey.

409
01:12:28.020 --> 01:12:49.720
Hilal Arslan: But also, we added to child and early and forced marriage indicators to the domain of violence. And this data that. We had it from the the Demographic, and had survey Turkey Demographic, and us survey that. I also took part in. It's like this is also collected by

410
01:12:50.070 --> 01:13:01.089
Hilal Arslan: Institute of Population studies. And but this is the data also 2,018, that we collected to data, and

411
01:13:01.130 --> 01:13:29.070
Hilal Arslan: and the the third one the feeling safe at night. Indeed, this is the the did this the data for this indicators, collected yearly by the truck St. It's a life inspection survey. And so this is like most currently available. And all the dates are that we indeed, we collected data for the last 10 years until 2,000 and 2,020.

412
01:13:29.070 --> 01:13:47.420
Hilal Arslan: So this is between 2,010 and 2 2,020. We had all the data for all Oecd countries in Turkey, and you are right that since, as a country, even officially, the agenda recalls the index one

413
01:13:47.420 --> 01:14:16.170
Hilal Arslan: try to calculate for Turkey. But this is not achieved yet. There are some like official problems in restrictions, and I know my, I know, from the insight, from the tricks that they started again the work to calculate this index. But until now we had our alternative index, and we want to continue to calculate it. Thank you very much.

414
01:14:17.900 --> 01:14:19.300
Any other question.

415
01:14:21.270 --> 01:14:44.760
Okay, then, I have a couple, if if I can. Now the the first, the question is relating to the fact that you said that you took inspiration from ages general quality index. basically, you you worked on national data. So it is quite easy to recover data for a European Union.

416
01:14:44.820 --> 01:15:06.040
I. I have seen that you also recovered data for Australia, New Zealand, and Japan. And you said, the Korea, etc. did you find all the same indicators, the same data that, jay, used, or did you have to do some adaptation

417
01:15:07.010 --> 01:15:31.170
Hilal Arslan:  in these we use the Oecd data but like when we try to first to select the indicators we check the data and metadata sources for a metadata, information for all the sources that is available for the countries. And we, Prefer to use the same data source

418
01:15:31.170 --> 01:15:59.769
Hilal Arslan: for all countries in order to make it comparable, and on that across the countries. So this was really important question. Thank you very much. I think when you try to calculate this kind, this type of index. And if you have a claim that it would be an international index done, we had to rely on the same incredible data sources and all the metadata information is available.

419
01:15:59.770 --> 01:16:23.229
Hilal Arslan: that I can share with you both in gender data portal that we have, and also in our reports. So that's why, like, usually, we use the national the data sources like income and living conditions survey or European households

420
01:16:23.230 --> 01:16:51.240
Hilal Arslan: and but just surveys, these are all Sundar surveys, and and that also Turkey is included like in your such surveys, and for the old, other countries. We rely on the like, you on population, Asian statistics, C and CD, or or Unic or World World organization databases that we checked from their their statistical offices. So we did

421
01:16:51.240 --> 01:17:10.940
Hilal Arslan: our best to make. It's like much more comparable. And you try to use the credible sources. And if it is a survey data, not administrative data, but survey data, we try to use the same survey data for the indicators.

422
01:17:10.940 --> 01:17:21.329
The other question that I wanted to do is the following, how did you compute the domain specific indicators and the synthetic indicator?

423
01:17:21.930 --> 01:17:30.569
Did you use any aggregation technique? Did you compute the rheumatic geometric mean? What did you do?

424
01:17:31.250 --> 01:18:01.199
Hilal Arslan: Yeah, exactly. We calculate to like the way to Jim and J. Ge. Geometric means. And also we had some like in these we had up to aes methodology like for some indicators. We had a like the best country, and we try to also calculate it's distance, and we rates it, and then we at the end. We ran to countries according to this.

425
01:18:01.540 --> 01:18:11.930
Hilal Arslan: but I think that was what was what was the for our methodology? The most important thing is that to decide on the indicators?

426
01:18:11.930 --> 01:18:35.200
Hilal Arslan: Because for 6 symptomatic fields that we work in, we nearly they will not like 1,300 indicators. But there are some indicators that the data is available, and there are some indicators that the data should be, collect us. And among the group of experts working on this indicators like

427
01:18:35.210 --> 01:18:44.649
Hilal Arslan: exports working in the field of education or health or justice like we discussed and to choose to like the best

428
01:18:44.690 --> 01:19:10.410
Hilal Arslan: and they, they, the most practical and data available available indicators, and also for our met to the right-based methodology. We try to like to indicate the gender based, dis indicate a choose the indicators which most represents a gender based discrimination in that the peculiar fields

429
01:19:10.750 --> 01:19:28.350
Hilal Arslan: but a, except that, in statistically speaking, after that, like, after we developed our index, we also conducted try to conduct an exploratory factor, analysis

430
01:19:28.350 --> 01:19:41.699
Hilal Arslan: to to check the domains like these are the correct, more or less. We had the same same domains after the because at the beginning we didn't use the Pca.

431
01:19:41.780 --> 01:19:57.320
Hilal Arslan: Or like the factor analysis to make the index. But as I said that we use the A guess methodology. But then, when we all finished the index, we conducted a like we did an F,

432
01:19:57.400 --> 01:20:16.709
Hilal Arslan: yeah, but we will again this year we will with the latest data we will calculate the next again. And I keep this in my mind like this. If I see it's still this confirmator factor. Analysis and is is in our mind.

433
01:20:16.980 --> 01:20:18.889
Okay, we have

434
01:20:24.820 --> 01:20:30.460
sorry. you follow the same methodology

435
01:20:30.780 --> 01:20:38.860
you use you. Yes, no. With a with the L. A. A. Alpha, and that things. Okay. Okay.

436
01:20:38.900 --> 01:21:02.679
yeah. They. that. That that point was also quite a criticized, because, it is an achievement level it. It is age Methodology is not so so basic. It is a quite, quite a complicated, also to be interpreted, not only to be applied. Okay, we have another question from the room.

437
01:21:05.530 --> 01:21:28.460
thank you for this presentation. my question was like, about, did you? compare your results of this index to other indexes, and maybe Did you so have the countries, a positions, for example, in your index, and also in the other existing indexes on gender equality. And to you find some differences, maybe.

438
01:21:28.470 --> 01:21:38.169
that are interesting because of some additional variables that you have. did you have any of this kind of conclusions. Thank you.

439
01:21:38.470 --> 01:22:03.950
Hilal Arslan: Hmm, and thank you very much for this question. Indeed, this was my last slide that, like, now we are working on. It's like to compare our index with, like others a generic quality indexes. And we had already, like work on the ndp Gender Equality Index. as a separate study for for turkey. But

440
01:22:03.950 --> 01:22:13.610
Hilal Arslan: and now we are working on it. And also we are working on to explain, like gender Equality Index with the other macro, political, social, and

441
01:22:13.610 --> 01:22:38.490
Hilal Arslan: economy indicators in the last 10 years of the turkey, whether it is it related or not. So this is our current work, and it I hope I hope we will publish the results as well. Thank you very much for the question. But I inside, I can say that, especially like in last week, global gender get indexes.

442
01:22:38.490 --> 01:22:50.529
Hilal Arslan: Now, the latest report release. And when I check to like the results of the survey. I can say that, like the older domains, more or less similar, like many indexes.

443
01:22:50.530 --> 01:23:19.460
Hilal Arslan: like the the health is like. For instance, the Turkey performs the best, the domain of huts, and we had similar indicators, and we know that also, like the first presented professor, I think, has a like good remark on the Turkey education issue. And then we also monitor the education inequalities in education, and this field, also more or less similar with the other in international indexes. With our index, I can say.

444
01:23:20.930 --> 01:23:21.890
Okay.

445
01:23:22.090 --> 01:23:27.199
thank you. Thank you so much, and no, no more questions. Thank you.

446
01:23:27.710 --> 01:23:29.230
Thank you.

447
01:23:31.100 --> 01:23:44.109
No. Now we have a time to to see the the video, the recorded, the presentation. I have to understand how I can do this. Probably at first I have to share the screen

448
01:23:44.940 --> 01:23:48.220
the then

449
01:23:48.320 --> 01:23:50.809
pre recorded.

450
01:23:51.390 --> 01:23:54.110
But it is not the no, okay.

451
01:24:01.540 --> 01:24:03.930
Can you hear it at the home?

452
01:24:03.940 --> 01:24:07.080
And it's

453
01:24:10.730 --> 01:24:11.400
yes.

454
01:24:12.850 --> 01:24:15.039
my, the room is very low.

455
01:24:24.570 --> 01:24:26.720
I okay.

456
01:24:27.290 --> 01:24:29.269
just a second. We stop.

457
01:24:29.890 --> 01:24:30.620
Yeah.

458
01:24:39.740 --> 01:24:48.820
But it is the out your room that we want to.

459
01:25:00.320 --> 01:25:01.529
But a

460
01:25:01.890 --> 01:25:11.000
so sorry. I I think I will use this time to be something, because there is no breaking for a next session. It it was lucky in session, and the. But the basic.

461
01:25:11.100 --> 01:25:25.469
Okay, keep in touch. Okay, see? You bye, bye, bye.

462
01:25:25.880 --> 01:25:36.210
this is a pre-record, the enterprise. Yeah. A lot of the a lot of the free proportion is very well. Let me see what I can do over here. Okay.

463
01:25:37.410 --> 01:25:38.250
at that.

464
01:25:38.640 --> 01:25:41.179
Yeah, this is a nice.

465
01:25:41.700 --> 01:25:43.049
Let's pause it.

466
01:25:43.830 --> 01:25:51.639
Yes, unfortunately, a lot of the prerecords have been recorded very low. Sorry.

467
01:25:53.140 --> 01:26:20.929
Okay, let me just check the volume on this. Yeah, they are going to post all of the pre recorded presentations on site or on the meeting app. They'll be posted outside of the session long recordings, so you'll be able to find them underneath each author. So the volume here is turned up. The volume on his video is turned up, and you have the volume over there cranked all the way.

468
01:26:21.750 --> 01:26:28.349
So we can try it again. Let's try it again one more time and see.

469
01:26:31.650 --> 01:26:32.660
yeah.

470
01:26:33.200 --> 01:26:36.830
it's unfortunate

471
01:26:38.320 --> 01:26:39.200
for some.

472
01:26:41.520 --> 01:26:43.220
Yeah.

473
01:27:00.640 --> 01:27:02.120
Oh, there.

474
01:27:03.380 --> 01:27:04.260
that.

475
01:27:05.930 --> 01:27:07.390
Okay.

476
01:27:07.460 --> 01:27:09.920
it's okay. We stopped again.

477
01:41:05.390 --> 01:41:06.410
Thank you.

478
01:41:08.010 --> 01:41:09.650
Huh?

479
01:41:10.160 --> 01:41:31.440
Okay, probably I'm sorry the from from remote. You couldn't here. however, the presentation is recorded so you can upload the system and the seat one comfortable for you. Sandra, do you have a

480
01:41:31.530 --> 01:41:32.870
flatter condition?

481
01:41:32.990 --> 01:41:49.299
Okay? Because because we are already when you see this video? Because this is also a video. Yeah, it's recorded. Thank you very much for your presentation. We know very well the topic because of.

482
01:41:49.300 --> 01:42:10.789
And Leo is our student in the master, and then we work a lot with that. And as also since the first time I recommend you, I know that you always said that the Homo nationalism is a particular literature, and is the name. But Also.

483
01:42:10.930 --> 01:42:15.739
again, I I I would like to to

484
01:42:15.970 --> 01:42:43.429
to suggest you to to change the name because someone national is not. Reflect the the the the indicator shows, and you can show the mix of homo. homophobia and er racism, and maybe the name, the very good name, and your own name will be homo racism, but not homo nationalism, because it is nothing

485
01:42:43.430 --> 01:42:47.030
to account about the the Nationalist.

486
01:42:47.030 --> 01:43:14.380
but I know, as you are very, very involved with this measure, and has an important background in literature. But even though I strong recommend to to work in the name, because you have done a very good, very, very interesting work on that.

487
01:43:14.580 --> 01:43:26.629
The previous one that we know very well that is quantitative, and this that is a Delphi. And thank you very much for your work and your that. You continue working on that. Thank you.

488
01:43:31.050 --> 01:43:43.840
So thank you. To all the participants. I'm sorry you couldn't hear the the video. However, it is recorded. So it is a viable

489
01:43:43.850 --> 01:44:13.499
 any consideration before concluding the session. Okay, I repeat again, the limitation to to join at a C. 55. It is a a good group We are now also trying to enlarge of activities. because of the group is not very wide the like groups because we are approximately 70 approximately

490
01:44:13.550 --> 01:44:34.809
But, for example, now we would like to start them a collaboration on a Phd programs in order to have joint initiatives. And also we could start well, we already started working on a European project. So together, most of us are European, but not only European. We have people from Japan at Thailand,

491
01:44:34.810 --> 01:44:43.919
Taiwan. Of course, Australia and Germany. Thank you. Journey for everything. This is the last day of the last. Thank you.

492
01:44:43.920 --> 01:45:07.839
And so really, I invite you to join. You will find a a a good a good good group of people collaborating. So we can conclude here the session. Thank you very much to the participants in presence online, I hope. I will recover soon, and see you next time.

493
01:45:09.390 --> 01:45:10.840
Bye, bye, thank you.