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Enregistrement W4389196130 · doi:10.1145/3632295

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

2023· article· en· W4389196130 sur OpenAlex

Pourquoi ce travail est dans la base

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affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueACM Transactions on Computing Education · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueImpact of Technology on Adolescents
Établissements canadiensImpact
Organismes subventionnairesnon disponible
Mots-clésSocial capitalPublic relationsFinancial capitalEquity (law)Economic growthBusinessSociologyMarketingPolitical scienceEconomicsHuman capitalSocial science

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,864
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0040,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,350
Écart entre enseignants0,331 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle