Bibliographic record
Abstract
In the wake and work of the Truth and Reconciliation Commission’s calls to action and movements like #metoo, there is a growing recognition that, as academics, we ought to care—not just for our research and our students, but also for each other and our communities. This paper begins with the notion that it is critical that we begin to research and understand caring and experiences of caring in leadership spaces in higher education and shares preliminary findings from a narrative inquiry study that asks two main questions: How do academic leaders who practice feminist care ethics experience their work lives? And how can knowledge about these specific lived experiences make visible and address a gap in understanding about the ways in which higher education landscapes can be navigated beyond dominant institutional narratives? This paper reports on findings from the first research conversations with study participants during which sharing of memories and stories of their experiences of “care” in higher education settings is anticipated.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.039 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".