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Record W3214811024 · doi:10.24908/jcri.v6i1.12486

Cultural Wealth

2019· article· en· W3214811024 on OpenAlexaffvenueabout
Sharon L. Brown

Bibliographic record

VenueJournal of Critical Race Inquiry · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMentorshipCultural capitalExperiential learningSociologyElitePublishingSpiritualityPedagogyPsychologySocial scienceMedical educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Graduate students who come from a background where neither parent has pursued higher education or specifically, who are Women of Colour (WOC), lack important cultural capital that could otherwise threaten or derail their pursuit of a doctoral degree. Yet, even with this prior familial knowledge, WOC still succeed because they depend on their developed cultural wealth (CW) to navigate through their doctoral studies. To thoroughly analyze this assessment, a theoretical framework that included: critical race theory, cultural capital theory, and Womanist theory was implemented. A six-item structured instrument was utilized to examine the educational experiences of 10 WOC doctoral students who were attending an elite Canadian university. The aim of the survey was to assess how these diverse students cultivated unique forms of CW through the telling of their stories. An analysis of the data revealed six categories of cultural wealth that were significant and instrumental in graduate student achievement. These elements were: 1) Mother’s Influence, 2) Age Capital, 3) Survival Strategies, 4) Navigating Academic culture or “Know- How” 5) Mentorship, and 6) Spirituality. The majority of the participants interviewed acquired; grants, publishing and funding opportunities, possessed all six components. Findings of this study suggest that experiential/cultural knowledge of WOC is valuable and important for further research in higher education; and that academic supervisors and administrators should consider using cultural knowledge as a guide and tool for practical mentorship, academic development, and supervision to ensure successful outcomes for current and future diverse students, especially for WOC in doctoral programs.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.073
GPT teacher head0.514
Teacher spread0.441 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2019
Admission routes3
Has abstractyes

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