Called to speak up: BIPOC women academics’ sense of power, calling and constructive voice
Bibliographic record
Abstract
Black, Indigenous and People of Colour (BIPOC) women academics face a paradox in higher education – tasked with championing diversity and critical inquiry, yet their voices are often silenced and their labour undervalued. This Interchanges piece examines how intersecting oppressions shape BIPOC women's sense of power, calling and capacity for constructive voice. Drawing from empirical and theoretical scholarship, we illustrate that despite systemic barriers, BIPOC women's calling motivates their intellectual activism to resist their collective marginalisation. However, this calling is frequently instrumentalised by institutions, where their services, particularly in Diversity, Equity and Inclusion, mentorship and care work, are undervalued or unrecognised. We call for institutional reforms to acknowledge and redistribute this labour, ensuring that calling is sustained as empowerment rather than exploitation.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.032 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".