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