Employable Wealth: Reframing Community Cultural Wealth as Employability for Students of Color in Higher Education
Bibliographic record
Abstract
Institutions of higher education have increased their focus on their students’ post-graduate outcomes, primarily these students’ ability to become successfully employed after graduation. Known as employability, many researchers have explored how postsecondary institutions do or do not prepare students for the world of work. However, many conceptions of employability do not adequately capture the lived experiences of students of Color, many of whom have been minoritized from the higher education system, and thus, do not access gainful post-graduate employment at the same level as their White peers. For this reason, we argue that traditional models and practices related to post-graduate employability in higher education must reframe ideologies surrounding employability, namely Yosso’s (2005) community cultural wealth model, which aligns nicely with and expands extant theoretical and conceptual frameworks. We then address directions forward and implications for policy and practice to support students of Color on their path toward post-graduate employment.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".