Bibliographic record
Abstract
This article showcases the authors’ personal journeys of building trust with their teams as BIPOC women leaders in the academic libraries. The article attempts to focus on trust-building with team members through changes either at personal level or at a larger professional context level. This paper elucidates challenges and opportunities that BIPOC leaders are experiencing in their roles in academic library settings as well as how junior and mid-level leaders develop their skill sets to facilitate their effort in trust-building. Literature searches show that study and research in the area of BIPOC leadership in the library context are sparse. The authors strive to contribute to the area, share the experience of building trust in their teams as a leader and invite BIPOC peers to explore possibilities of building trust and advancing their careers in the library leadership path.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.035 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".