80.5619.44 75.00 25.0041.67JSTE1.46539.47 60.5382.4617.54 66.67 33.3326.32RISE2.24852.94 47.0611.7688.24 80.39 19.6119.61Abbreviations: CSSE,Cultural Studies for Science Education; IJSE,International Journal of Science Education; JRST,Journal ofResearch in Science Teaching; JSTE,Journal of Science Teacher Education; RISE,Research in Science Education.BANCROFTET AL.|1241 1098237x, 2022, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/sce.21733 by Univ of Colorado Health Science Center, Wiley Online Library on [03/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

double‐blind peer‐review process. Each of these approaches to peer‐review and their advantages and disadvantages are presented later in the paper.

ANALYSIS

The research questions (RQs) guiding this paper are:1. How does the described peer‐review process compare across well‐established science education journals?2. What is the extent of the diversity of the editors and editorial boards across well‐established science educationjournals using peer review? To what extent are scholars who pursue equity‐focused research represented?3. How do the included journals support reviewers' ability to provide a fair review?

Homophily is a tendency of reviewers to positively review scholarship of authors who are in their same socialgroup

Cognitive particularism is a tendency of reviewers to more positively review scholarship like their own

cognitive particularism (Travis & Collins,2016) andhomophily

likely thriving compared to their male counterparts indisseminating their research in venues where peer review acts as a gatekeeper.

women represent 75.5% of 2020 doctoral recipients in the fieldof science education

Unbiased evaluation of the manuscript can be promoted in the peer‐review process when editors assignreviewers who use similar epistemologies

Publishing

bias in the peer‐review process

case

Although research suggests that social-emotional qualities have a positive influence on academic achievement, most related studies examine these qualities in relation to outcome measurement and prediction, and more work is needed to develop interventions based on this research (Levin 2013)

seven pre-processing tasks

two research goals are priorities for studies of social and emotional learning: 1. Developing assessment techniques, 2. Providing intervention approaches.

The movement involves some complex issues ranging from the establishment of social and emotional learning standards to the development of social and emotional learning programs for students, and to the offering of professional development programs for teachers, and to the carrying out of social and emotional learning assessments (Kamenetz 2015).

The field of statistics is the core building block of DS theory and practice, and many of the techniques for extracting knowledge from data have their roots in this.

The well-defined model presented in this work can help ensure the quality of results, contribute to a better understanding of the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Second, a case study of social-emotional learning is presented. We hope the issues we have highlighted in this paper help stimulate further research and practice in the use of data science for education.

Each of thesefive strands are simultaneously present in our reflection

implicit biases re-lated to the values stemming from which systems of ethics we prioritize

DIALOGUE 2: Journey

KST: Shí éí Kinłichii’nii nishłi ̧′dóó Naakai diné’e bashishchiin. Tòdichii’niidashicheii dóó Tłizi’łaní dashinalí. ̇Akót’éego diné asdzáán nishłi ̧′. (Englishtranslation: I am Red House clan, born for the Mexican Peoples clan. Mymaternal grandfather’s clan is Bitterwater and my paternal grandfather’sclan is Many Goats. In this way, I am a Navajo woman.

Belin

hí éí Ilocano nishłi ̧′dóó Ta’chii nii bashishchiin. Ilocano dashicheiidóó Tsi’naajinii dashinalí. ̇Akót’éego diné nishłi ̧′. (English translation: I amIlocano/Filipinx, born for the Red Running into the Water clan. My mater-nal grandfather is Ilocano and my paternal grandfather’s clan is Black Streakin the Forest.)

he intersections sections at the end of each dialog are intendedto highlight the themes that we collectively recognized in our own answers.

questions

reveal interactionsbetween narratives and identities that intersect in a particular cultural space

as opposed to some social construct primarily used forindividual reflection

six major subject areas for thedata science majo

researchers towards considerations of a data science education that is equity-minded both in concept and practice

uoethnography — a research method in which prac-titioners discursively interrogate the relationships between culture, context, andthe mechanisms which shape individual autobiographical experience

authentic community partnership takes time

Black x Male to identify a Black male, instead of looking at that person’s identities in isolation

classified as white

Giving communities a voice in creating and shaping that data is essential for using data with a social justice orientation

Repositioning black girls in mathematics disposition research: New perspectives from QuantCritRACE ETHNICITY AND EDUCATION5

A QuantCrit analysis of the Black teacher to principal pipeline

novel or underutilized

ole of community

2010 through 2022. Twenty-seven studies fit the criteria.

systematic review of enacting quantCrit

extensive gap between Black students and either White or Asian and International students in their ability to persist.

Latinx, non first-generation females have the highestpersistence

Black, first-generation males have the lowest probability of persisting in any major is supported by research on Black male retention in higher education literature

The students in the sample have a slightly higher SAT critical reading score than mathematics score, have a mean cumulative grade point average (GPA) of almost 3, and a mean total number of semestersof about seven.

five principles

high school grade point average

intersection

16% of undergraduates in the USchose STEM as a career major, compared to higher percentagesfor other countries(up to 47% in China), which can not only be detrimental to the USeconomy, but to the global market (National Science Board, 2010

QuantCrit theory, we use multilevel modelsto determine factors that predicted persistence in any major and factors that predicted persistence in STEM. We also use marginal effects to explore the intersection ofethnicity, gender, and first-generation status

(2003-2013) tracking of a census of 53,077students

Society’s educational debts before instruction were largeenough that women and Black men’s average posttest scoresdid notreach the White men’s average pretest scores.

acism and sexisminterac

ismiss meaningfulinequities due to lack of representation in minoritizedgroups.28Instead, we used the standard errors of the estimatedscores to inform our confidence in the results

social identifiers

dson-Billings’11,12concept of“educational debts”to interpret the extent towhich college chemistry courses mitigate, perpetuate, orexacerbate the educational debts society owes to studentsfrom groups minoritized13by racism and sexism.

QuantitativeCritical (QuantCrit) perspective that framed inequities as educa-tional debts that society owed students due to racism, sexism, orboth. Results showed that society owed women and Black menlarge educational debts before and after instruction. Society’seducational debts before instruction were large enough that womenand Black men’s average scores were lower than White men’s average pretest scores even after instruction. Society would have toprovide opportunities equivalent to taking the course up to two and a half times to repay the largest educational debts. Thesefindings show the scale of the inequities in the science education systems and highlight the need for reallocating resources andopportunities throughout the K−16 education system to mitigate, prevent, and repay society’s educational debts from sexism andracism

QuantCrit principles, and the examples we have set out above, will go some way tosupporting greater critical scrutiny of quantitative data and the potential to harness its status in the cause of social justice

Critical race theorists work simultaneously withand againstrace, (i.e.,we know that race only exists as a social construct, but we recognize the,sometimes murderous power of the fiction and seek to engage, resist and ultimately destroy race/racism). Similarly, QuantCrit should work withand againstnumbers by engaging with statistics as a fully social aspect of how race/racism is constantly made and legitimated in society.

Don’t accept numbers on trust. Ever.

Social relationships are hugely complex and fluid; they do not easily translate into simple categories and effects that are easily quantified.

peopleare used to judging the trustworthiness of qualitative data

‘Crime Statistics Bureau’ doesn’t exist.

We also know that researchers are encouraged to search for “silver bullets” or universal approaches in their work. In fact, we still fail at upholding all of the recommendations we have offered. However, understanding the value that statistical practices have in equity policy initiatives, we are committed to working through present-day limitations that come with the quantification of human experiences. By being upfront where our work falls short, we get closer to discovering new analytical approaches that can be used for liberatory purposes.

Question

The foundational elements of QuantCrit are tied to critical race theory but are also aligned with other perspectives of the critical canon. Critical race theory explores where and how racism prevents people of color from accessing social and economic opportunities (Bell, 1995; Ladson-Billings, 2009). Critical race theorists are also interested in subverting defi-cit-framing projections by documenting the ways that people of color actively resist and cultivate joy despite racist structures

In contrast to postpositivism, a critical lens assumes that what can be known about the world is socially constructed. Critical theorists separate themselves from traditional theorists across fields (Bohman, 2005). Critical theories explore how his-torical events and society have shaped present-day experiences and understandings of how the world functions

these devastating and brutal events reflect under-lying systemic inequities that exist across the globe

experiences

socially constructed and fluid.

ensure diversity in the narratives promoted.

intersection of these axes shapes an individual’s experience of the world.

s that all data and analysis methods introduce biases and strives to minimize and explicitly discuss these biases.

QuantCrit researchers commit to disrupting narratives that frame minoritized students as deficient and disrupting oppressive systems through anti-racist and anti-sexist work.

Who benefits from data science and who is overlooked.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1A they/them Pollicino?

Seals Allers—and her fifteen-year-old son, Michael—are working on their own data-driven contribution to the maternal and infant health conversation: a platform and app called Irth—from birth, but with the b for bias removed (figure 1.8). One of the major contributing factors to poor birth outcomes, as well as maternal and infant mortality, is biased care. Hospitals, clinics, and caregivers routinely disregard Black women’s expressions of pain and wishes for treatment.81 As we saw, Serena Williams’s own story almost ended in this way, despite the fact that she is an international tennis star. To combat this, Irth operates like an intersectional Yelp for birth experiences. Users post ratings and reviews of their prenatal, postpartum, and birth experiences at specific hospitals and in the hands of specific caregivers. Their reviews include important details like their race, religion, sexuality, and gender identity, as well as whether they felt that those identities were respected in the care that they received. The app also has a taxonomy of bias and asks users to tick boxes to indicate whether and how they may have experienced different types of bias. Irth allows parents who are seeking care to search for a review from someone like them—from a racial, ethnic, socioeconomic, and/or gender perspective—to see how they experienced a certain doctor or hospital.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Amanda Christopher.

This consists of asking who questions about data science: Who does the work (and who is pushed out)? Who benefits (and who is neglected or harmed)? Whose priorities get turned into products (and whose are overlooked)? .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Seyoon Ahn

We use the term power to describe the current configuration of structural privilege and structural oppression.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }2A they/them Pollicino, Jiaqi Qin, in which some groups experience unearned advantages.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1111—because various systems have been designed by people like them and work for people them—and other groups experience systematic disadvantages—because those same systems were not designed by them or with people like them in mind. .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }211

examine power is the first principle of data feminism, and the focus of this chapter..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Khushi Loomba

On the contrary, the data showed that Black women with college degrees suffered more severe complications of pregnancy and childbirth than white women without high school diplomas.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }11..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }61212

Black women are over 3 times more likely than white women.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }2Eshan Velidandla, Nishaan Chavla to die from pregnancy- or childbirth-related causes.”2.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1111

Measures

et the field’s understanding of racial and ethnic dispari-ties in advanced STEM achievement during elementary school is currently limited. Relatively few studies of advanced STEM achievement have been conducted, particu-larly those using elementary school samples and longitudinal designs (Clotfelter et al., 2009; Davis-Kean & Jager, 2014; Gandara, 2005; Rambo-Hernandez et al., 2019). Of these, only two studies have examined racial and ethnic disparities in advanced STEM achievement as early as kindergarten in analyses of nationally representative data (Davis-Kean & Jager, 2014; Gandara, 2005). Neither study reported on explanatory factors for these disparities in adjusted analyses. Existing studies examining advanced STEM achievement have analyzed samples of middle or high school students (e.g., Kotok, 2017; Lubinski et al., 2014; McCoach & Siegle, 2003) or examined gender disparities (e.g., Penner & Paret, 2008; Robinson & Lubienski, 2011).

An antecedent-opportunity-propensity framework is a well-validated theory of achievement growth (Byrnes, 2020) hypothesizing that a relatively small set of student, family, and school factors explain racial and ethnic disparities in STEM achievement

An antecedent-opportunity-propensity framework is a well-validated theory of achievement growth (Byrnes, 2020) hypothesizing that a relatively small set of student, family, and school factors explain racial and ethnic disparities in STEM achievement

Addressing racial and ethnic underrepresentation in the sci-ence, technology, engineering, and mathematics (STEM) workforce is a national priority (American Society of Mechanical Engineers, 2021; National Academies of Sciences, Engineering, and Medicine [NASEM], 2011; National Science Foundation [NSF], 2021). Less than 10% of the U.S. STEM workforce is Black or Hispanic1 (Funk & Parker, 2018; National Science Foundation [NSF], 2019). White or Asian students are more likely to complete STEM college degrees (Steenbergen-Hu & Olszewki-Kubilius, 2017). Less than 1% of those with a bachelor’s degree in sci-ence or engineering are American Indian, Native American, or Pacific Islanders (AINAPI). The contrasting percentages for those who are White are 57% and 64% (NSF, 2021). The nation’s economic competitiveness and scientific innovation is constrained by racial and ethnic underrepresentation in the STEM workforce (Bell et al., 2019; NASEM, 2011). The earning potential of high-achieving adults of color is also constrained. High-achieving college students of color major-ing in STEM report early career earnings that are 26% to 40% higher than closely matched counterparts majoring in other fields (Melguizo & Wolniak, 2012).

Research Question 1: Are Black, Hispanic, or AINAPI students less likely than White students to display advanced science or mathematics achievement during elementary school? If so, how large are the observed gaps?Research Question 2: Do antecedent, opportunity, and propensity factors explain the lower likelihoods that Black, Hispanic, or AINAPI students display advanced science or mathematics achievement during elementary school?

Greater use of special education data may help prevent future systematic failures to identify and serve eligible students with disabilities

percent of students in special education is negative and significant

special education accountability system to reduce the number of students identified as children with disabilities.

level because pov

chievement gap

mpede their school success

Transparency matters.

So theprisoners are kept in the dark as much as possible and do not learn theirrisk scores

But in others, includingIdaho and Colorado, judges use the scores to guide their sentencing

The question, however, is whether we’ve eliminated human bias orsimply camouflaged it with technology

acism operates like manyof the WMDs I’ll be describing in this book

And once their model morphs into abelief, it becomes hardwired.

key component of every model, whether formal or informal, is itsdefinition of success

A model’s blind spots reflect the judgments and priorities of itscreators.

When we ask Google Mapsfor directions, it models the world as a series of roads, tunnels, andbridges. It ignores the buildings, because they aren’t relevant to the task.When avionics software guides an airplane, it models the wind, the speedof the plane, and the landing strip below, but not the streets, tunnels,buildings, and people.

A model, after all, is nothing more than an abstractrepresentation of some process

redictive mathematical modeling

crude data, most of it observational

humans that are really biased,”

17.7% of patients that the algorithm assigned to receive extra care were black. The researchers calculate that the proportion would have been 46.5% if t

systemic racism, ranging from distrust of the health-care system to direct racial discrimination by health-care provider

verage black person was also substantially sicker than the average white person

assigned lower risk scores than equally sick white people.

urces and closer medical super-vision for people with mu

less likely to refer black people than white people who were equally sick to programmes that aim to improve care for patients with complex medical needs

we employ the term feminism as a shorthand for the diverse and wide-ranging projects that name and challenge sexism and other forces of oppression, as well as those which seek to create more just, equitable, and livable futures.

hocked at the disparity,

wealth inequalities and the role that well-educated, well-off people play in maintaining those..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; } Or to believe in the logic of co-liberation. Or to advocate for justice through equity. .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1nyah beanIndeed, a central aim of this book is to describe a form of intersectional feminism that takes the inequities of the present moment as its starting point

ngineering positions, where they could be promoted through the ranks of the civil service, while women with the same degrees were sent to the computing pools, where they languished until they retired or quit.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }211..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Joe Masnyy