What is QuantCrit? w/ Dr. Wendy Castillo का पूरा ट्रांसक्रिप्ट

try to do it but. You know I. In thinking about this wonder if my and this is something that I think about a lot is like is my taking up space subtracting from the space that other people could be taking up. Rdb: [8:26] Hmm. Sss: [8:28] And it's just so hard to know if your motives are kind of pure you know I think I worry about this all the time I think about this with my work and are we participating in this thing because we think that. [8:45] We'll get something out of it for ourselves that it will say something about ourselves versus are we participating in it because we actually want to do good and help support change and I think that in many ways that ends up being you know because human beings are complex and there's a lot going on inside each of us it ends up being a cocktail of all those things together right it's hard to pull one thing apart from another, and I think about my time and the reasons that I got into education who I was when I started teaching, more than 10 years ago the things that pulled me in I'm a different person now than I was then but looking back on that person some of my motivation was a sense that I knew what was I knew how to fix things. I had a sense of how to make things better and I think that's a bias that I bring to my work sometimes is that I think I know how to how to fix problems. And. [9:38] That I mean I may not know or whether I know or not as kind of a side the point it's that feeling that I know and that therefore I should impose that fix. Where I can and I think that there are aspects of that that are that are positive and helpful and I've drawn that and I try to be helpful. But I think that there are aspects of that that come from a place of wanting to project being a certain kind of person and I feel like I'm getting off into a little bit of where I'm tethering myself from a specific conversation. Somewhere in the like psychology and. Something a little bit beyond this debate topic but I think that's a bias that I bring. Nal: [10:18] Are there any biases that we held together as a group of podcasters. Rdb: [10:29] I definitely think so for sure I believe that we are privileged in that we have the education that we have and. I'm sure all of y'all feel the same way that I do like as educators. Because our boots were on the ground at some point or are still are. Sometimes we tend to think that we can be experts on certain topics because we've dealt with them. Specifically from students but I have to remind myself especially when educational leaders are speaking. Um and they haven't been in the classroom I have to really be careful and think you know like even though we've been in the classroom and a lot of our knowledge is important it's still one View, it's one classroom one view of a certain body of students versus all the kids in the country and so we can also stand to learn a lot to about education I try to frame it as perspective. Now instead of like my knowledge my experience in the classroom and I try to now say like my perspective. Because it's different. Nal: [11:47] It really is. Sss: [11:48] Honest way. Nal: [11:51] That's exactly right to even though on all of our episodes we probably come across sounding like we have knowledge or we think that we have knowledge what we are really bringing is just perspective and, hopefully you know we bring in experts because we want to acknowledge the things that we don't know and to learn from them so. Just to put this out there the three of us were all teachers we are holding a little bit of that teacher bias we all. Had a college education and we hold that the biases that come with that and certainly I think we all value education. To some degree so we're going to keep those in mind as we continue this podcast and as always remember that we will continue this conversation online we'd love to hear from some of our listeners about your own positionality, you know our guest this week dr. Castillo mentioned that this is an exercise that can be useful for not just teachers and researchers but for anyone, on a team or in a corporate setting to spend a little time considering what might be your personal biases, and if you do write one of these positionality statements please share it with us send us an email and we'd love to continue this conversation so now we're going to move into the segment where we listen to the interview with dr. Wendy Castillo. Music Interviewer: [13:29] Okay so let's get into these five pillars of quantitative critic because I think people do have an idea that like numbers can be biased and understanding that but I think there's also a whole Camp of people that would say oh well numbers are numbers they can't be biased they exist in a neutral context so how do we. Argue against that with quantcrit. Guest: [13:50] Quantcrit has five principles five tenants the first one is acknowledging the centrality of racism which is really what critical race theory is you know the racist systems that exist in this country that perpetuate any qualities based on race that's the very first, tenant and in my paper I talked about how do you put this into practice, so the way you can think about putting the centrality of racism into practices thinking about a positionality statement that is something that is well known and written as a qualitative researcher but quantitative researchers never right one I don't want to say never but it is not common practice to right one but thinking about our own background and our own biases and how those May influence our data collection the development of our data instruments the way we analyze the data for example if my experience, with because I'm a Latina for me it's really important to think about, how Latinos are not a monolith and how within the Latino Community they're just inequities and disparities that exist and so. That is something that's really important to me so when I think about my position ality a lot of people like to lump

in. [15:18] All Asian people all black people but for me it's really important to disaggregate that data so. Practice that you can do is start collecting data that way so start collecting data in ways that are more nuanced and represent people's identities and then. How you analyze the data and present it later on the line those are other decisions that are going to be made and it's just really important to acknowledge your biases up front. And to re visit them over and over again as a team if you have a team of researchers to revisit what your Collective by sees our blind spots as a team maybe and that's how you can apply that. Interviewer: [16:07] Yeah that's that's really helpful to think about as an example of quantcrit what right is because. I think we're all sort of familiar with the classic. Ethnicity and racial background statistics which you see as right black Latin X Asian. And and then more recently you've seen like Native American Pacific Islander thrown in there and white but yeah it's very rare to see any desegregation, of those of those identities so have you seen I guess the in your work with researchers and funding different projects are people starting to push more for this this type of data collection. Guest: [16:51] The senses that was supposed to be funded that did not get funded was going to be more nuanced in that way. Interviewer: [16:59] The national then the like National. Guest: [17:01] A 2020 census yeah. But that did not happen I know that New York City collects this data I know that New York City Department of Education collects this data some Departments of Education collect that data I know that you see system in California collects really nuanced ETA of their students so I'm not sure what they're doing with the data or how they're analyzing it but they're the first step is to at least have it and so the U C system and the US Census was supposed to happen. Interviewer: [17:37] Well that's sad to hear that it didn't happen but I guess good to hear that it's started something that is being considered do you have any advice for how teams can like go about. Voicing their biases and relate like documenting and formalizing that. Guest: [17:59] Yeah I have written a positionality statement I think it changes depending on your project and you have to create a safe space within your team so for example if I was doing a project on higher education, versus a project on K12 my advice these might be completely different so if I miss doing a K-12. [18:19] Research study my background as a student was in public school education and underfunded School District so my biases and what I think of a school and what I think of as high-quality might, different verses in higher education I've only intended private schools and Elite institutions so my biases on what an experiences in higher education, might be different and it's just important for me to be up front with those, so when I'm doing research and it might be on state schools are underfunded knowing that I don't know what that experience is like because I never had it and making sure that I'm not making assumptions along the way that students had particular resources or didn't have particular resources either way because of My My by sees that I may have towards State schools based on my experience in Elite schools so it's just, being very open and having a safe environment I think it's important to document that and I think that a way to make people feel safe as to do it as a team so you can do an individual positionality statement, for you and I think that's when people are very comfortable was just explaining their biases but if as a team you have your vices and you don't feel singled out and I think that could be the first step. Interviewer: [19:31] Got it is this something that you think could be implemented in in secondary schools like would it be important for teachers to also write these positionality statements. Guest: [19:41] Yeah that's a really good point yeah I don't see why not I you can apply the same idea. To multiple settings even at work I can see that being applied I think about it in research because we're making you know decisions on, data so it's important to know how your decisions might affect your background but you know you're making decisions on how you are sorting students. So I think it would be important to have a positionality statement and with that I'm going to go to the second tenant which are numbers are not neutral um which again it's your making statements on you know sorting students and if you're making decisions based on a particular test s 10 is that numbers are not neutral, and that means that you can be using tests that you know are not intro so therefore there. The results of those tests on the data from those tests will not be neutral because they may be biased test and then who gets to take certain. [20:46] Test and go into certain classes like AP classes and be tracked all those things just funnel into that so numbers are not neutral because we as humans are deciding you know. What those numbers are were deciding what the denominator is who is counting in that denominator we're deciding to the numerator as who's counting in that average and we're deciding which test people are taking whether those tests are biased or not so. That's the second tenant which I think applies to teachers because. They make a lot of decisions based on formative data and it's important for them to take a step back and think about that formative data was. Formed by humans and it might be biased so you don't want to make you know an important life changing decision based on data that might be biased so you want to just re-evaluate it. Interviewer: [21:42] Wow yeah that's I mean especially in a climate of. Standardized testing like I feel like that's such a powerful way to apply this theory is to say like look these numbers or you needs a I keep coming back to this idea that it's not

necessarily like. A none of these are like hard and fast rules like don't do this do this it's more like. By keeping these things in mind by starting from a different place you gain a perspective that is going to inform you about. The inequality that's that that's impacting your students. Guest: [22:22] And the third tenant which is, categories are neither natural nor given for race three-d-- racism if your numbers or your tests are correlated, with race it's important to think about how it's not being black or being Latino or being Asian that makes you more correlated with higher SAT scores its racism it's a systemic racism that exists within the systems and within that test so how you read the data and how you present the data is really important when you see in your data whether it's formative. Assessments or like standardized assessments at the end of the year if they're related to race then it's really important for us to be thinking about how it's related to racism whether it's. The systemic racism or you know stereotype threat there's something in in that test in our schools in our systems that is making up certain race be related to that. To that score and it's not because you're born black that you're related that you're automatically you know related to lower SAT scores. Interviewer: [23:36] Can you say a little more about stereotype threat I think that's such an important concept and I want to make sure we don't miss that. Guest: [23:41] Yes so at least for me growing up. I was always good at math but no one ever told me and I never saw people that looked like me be good at math. [23:59] And I gave up on math in high school I never took math again and I think. I because I didn't see females being good at math because I didn't see Latina women being good at math I you know I fell into the stereotype that you know I'm just we're just not good at that, that's just not cool we are so that it's the idea of living up to these expectations of society, puts towards a particular group so. That's a huge one where women don't think they're good at math particularly women of color and teachers might. Also project stereotype threat I do feel like my teachers never. Sami as a bright mathematician I think they might have projected some of their own stereotypes of what a mathematician looks like on me and I might have internalized them and therefore decided I'm not good at math and I'm never going to be a mathematician so I stop taking math after my sophomore year where I took calculus, and I didn't take math again until graduate school. Music Sss: [25:20] If you're enjoying today's episode we'd love to hear from you feel free to send us an email or reach out on social media also don't forget to subscribe to our podcast on Apple Spotify or wherever you get your podcast. Nal: [25:35] And now it's time for data town this is. Music Nal: [25:41] One of our last data towns I'm going to describe a couple of tables that we have, for some data that came from a paper by some quantitative critical race Theory researchers and basically what they looked at here is some data that was collected on, hot college graduation rates and how, biases in terms of how the data was collected might be shaping or framing some of the opinions and conclusions that could be drawn from, so the first table is from the original data collection and its statistics on graduation rates it has two districts for female students white students black students Hispanic American Indian Asian and low-income students. It has their you know percent graduation rates from this college and the research took a look at this and then they presented a alternative table, that they say would improve on some of the biases that are present in the way the data was collected originally so their improvement shows data collected and presented for. Intersectional identities. [26:49] Basically looking at much smaller grain sizes to avoid grouping people together into some kind of these large monoliths because not all low-income students, are the same not all female students are the same right not all black students are the same and so what they did was they came up with smaller categories like white low-income women, white high-income men white low-income Man black high income women, and they listed out a much larger table but it gives us a much more granular look at this these graduation rates and really it allows us to pinpoint where there might be challenges where there might be places for improvement or you know systemic issues that are present at the schools and the researchers are pointing out that if we simply looked at the base level data we would never have gotten to these deeper. So I'm curious what you both think about this data collection and whether you think this is something that could be done. Going forward on larger scale. Rdb: [28:05] I feel like it's necessary I mean if you are I know people don't want to do it I know that when you're actually, I mean I've done it I've sat down and done statistics and it's hard it's hard when you factor in all these intersectional categories and, then you have to actually write narratives about all these correlations and everything and that's hard but I don't believe that, we really would be doing our research Justice even if you didn't care about people you wouldn't be doing your research Justice if you didn't really pick it apart as much as you possibly could, um and especially in education and educational research if you're gonna sink to serve students and you want to put out information. That other people. Are whether you like it or not going to take and create policy out of and put money towards or take away money from you better make sure that you're giving them the most information that they can work with. And maybe that's my bias talking right like maybe me as a former low-income black woman now as a higher income black woman. Knowing all of the different challenges that I faced in

different supports that I needed to get to the point that I am now maybe that's why I feel like this is so important but. I definitely champion this kind of you know very deep very complex research. Sss: [29:33] Yeah and you know I think the thing that this also really hits on is that, there's a lot of nuance when we talk about race or any Factor having an impact on some statistic like your graduation rate. [29:48] There's a lot of nuance that that is not captured by a single lens and so looking at more than one lens just gives us more of that information allows us to get at the details more and it. Prevent I think the kind of like confusion and Broad brush painting our broad brush stroke in that people like to do in arguments where they say you know. Black men this white men this there are statistics that are really helpful when we look at just that lens but when we break it down further a lot of stuff that I think people get upset about or confused about ends up kind of going away you can see that, obviously race and income have an impact and certainly so you know looking I'm looking at this data table right now the the. The more granular look is race and, income but we could go even further right and look at geography or neighborhood or there are so many different things that we could look at with this, and it helps to paint the picture that I think some people are afraid gets lost when we're talking about oh you just want to talk about race you just want to you know, look through this one lens it's like yeah I mean we do want to have that conversation but we also want to look at the new one so I love this for all that detail. Nal: [31:06] Yeah this is so important and we do will, put some of these tables out so that you can see them on Instagram Twitter because, what's important about these data tables is not just that the researchers are collecting this data right because there are places throughout the entire process from data collection to data analysis to data presentation at which many of these categories get reduced down and it might be that all this data was collected but then when the, you know a newspaper took this article and wrote a piece about it or you know someone synthesize this paper it to present it to like Raven you were saying you know a policy maker that they synthesized it down as it all we'll just group these numbers together it's like but when you do that. Music Nal: [31:51] You are reducing and you are erasing some individual lived experiences and that's that's what this is really about so yes the complexity is warranted, that brings us to the end of today's episode. Sss: [32:04] Don't forget to join Reddit or Twitter and share your opinion on this week's debate and education. Rdb: [32:10] And to learn more about this week's guests and to find out how to support this podcast visit learning machine podcast.com you can also follow ND Castillo on Twitter at w Castillo PhD. Nal: [32:24] Thank you to all those who teach listen and learn see you next time.