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Record W4389196130 · doi:10.1145/3632295

The Important Role Social Capital Plays in Navigating the Computing Education Ecosystem for Black Girls

2023· article· en· W4389196130 on OpenAlexaff
Camille Ferguson, Vanora Thomas, Juan Del Toro, Daniel Light, Kamau Bobb, P Clarke, Shameeka Emanuel, Ed Gronke, Mary Jo Madda, Imani Jennings

Bibliographic record

VenueACM Transactions on Computing Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsImpact
Fundersnot available
KeywordsSocial capitalPublic relationsFinancial capitalEquity (law)Economic growthBusinessSociologyMarketingPolitical scienceEconomicsHuman capitalSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.008
Scholarly communication0.0100.006
Open science0.0010.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0200.002

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.

Opus teacher head0.019
GPT teacher head0.350
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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