Perspectives: Cultural Wealth, Co-creation, and Conversations
Bibliographic record
Abstract
In this article, two librarians reflect on their experience as an intern and supervisor navigating traditional and antiquated norms upheld in academia. The intern, a first-generation Mexican-American student, describes the shift in her values, beliefs, and identity as she confronts the extractive practices embedded in internships, resulting in a collaborative and critical internship. The internship supervisor, a senior librarian, also reflects on her role in resisting these exploitative dynamics by using the community cultural wealth model, which recognizes the knowledge students of colour bring from their homes and communities. The intern and supervisor introduce ideas for improving the internship experience by embracing community cultural wealth, critiquing the role of neoliberal multiculturalism, and addressing the systemic extraction that hinders the professional development of marginalized students.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.038 | 0.048 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".