What is ideal EDI learning for academic librarians? Discovering EDI learning stories through appreciative inquiry
Bibliographic record
Abstract
Academic libraries across North America purport to be prioritizing equity, diversity, and inclusion (EDI), but investigations into how librarians learn about EDI are lacking. In this study, we interviewed 21 academic librarians in Canada about their EDI learning journeys using the strengths-based appreciative inquiry approach. This paper focuses on the question, “What shapes ideal learning experiences related to EDI for academic librarians?” In uncovering librarians' stories of learning transformations, we found that EDI learning often elicits discomfort; it involves recognizing one's biases, being vulnerable, and making mistakes. However, these learning stories can motivate and inspire others to learn and engage in critical self-reflection through questioning assumptions and underlying beliefs. EDI learning in professional contexts was inextricably linked to learning in informal and personal contexts, and positionality is essential to how learning is shaped. Learning was described to be ideal in low-pressure, authentic, brave environments that facilitated meaningful conversations, with institutional support. However, there seemed to be a disconnect between one's learning and one's ability to effect change.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".