The Important Role Social Capital Plays in Navigating the Computing Education Ecosystem for Black Girls
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Black women represent the greatest underrepresentation in STEM fields, particularly the technology sector. According to a 2015 article in The Verge , Black women make up between 0% and 7% of the staff at the eight largest technology firms in the United States. This points to a glaring problem in terms of equity and inclusivity in the technology sector. Similar to their underrepresentation in the STEM sector, Black women's underrepresentation in the tech sector is related to pervasive and persistent prejudice and biased policies that endure in the United States, which have limited—and continue to limit—their access to quality education and spaces where Black women's cultural capital (i.e., ways of being) is acknowledged and appreciated. For most people, including Black women, social networks often make available opportunities and pathways toward realizing the roles they can play in the world or a particular industry. These webs of relationships and the embedded quality in them can be defined as an individual's social capital and be applied to any industry, including STEM and technology fields. In a practical sense, social capital allows an individual to leverage relationships for resources (e.g., information about internships and jobs or encouragement to persist through a difficult college course). In turn, these resources can contribute to economic opportunities (i.e., jobs) or social opportunities, such as relationships with gatekeepers who work in STEM fields that may lead to opportunities like jobs, projects, or financial backing. Research suggests that the social networks of Black young women rarely overlap with the networks of predominantly White and Asian males, who are overrepresented in the technology field. This weakens Black women's awareness of opportunities and training, and undermines their motivation to persist in the STEM sector. As a result of this increasing understanding of the role of social capital in career development, K–12 and higher education programs that are focused on equity in STEM fields have increasingly turned to the concept of social capital to address the traditional underrepresentation of certain groups, particularly Blacks, Latinos, and women in STEM fields. The following research investigates the experiences of Black girls who attended a program, Google's Code Next, designed to engage Black and Latinx youth in computer science. We argue that it is crucial for computer science programs not just to teach hard coding skills but also to build on young Black women's social capital to accommodate the young women in creating and expanding their tech social capital, enabling them to successfully navigate STEM and technology education and career pathways. Specifically, this article explores a subprogram of Code Next and how it has contributed to young Black women's persistence in STEM, and particularly in technology. The findings suggest that the young women employed an expanded sense of social capital in addition to an expanded cultural capital (i.e., language, skills, ways of being) and worldview (i.e., sense of belonging and self-efficacy) to make sense of their possible selves in the world of technology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it