Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".