Key Insights into Factors that Shape the Ideal EDI Learning Experiences of Canadian Academic Librarians
Bibliographic record
Abstract
A Review of: Fitzgibbons, M., & Lei, C. (2024). What is ideal EDI learning for academic librarians? Discovering EDI learning stories through appreciative inquiry. The Journal of Academic Librarianship, 50(5). Article 102908. https://doi.org/10.1016/j.acalib.2024.102908 Objective – To gain insights into academic librarians’ learning about equity, diversity, and inclusion (EDI) to identify ideal learning practices, and to inform the development of EDI learning in academic libraries. Design – Appreciative inquiry-based semi-structured interviews. Setting – Canadian higher education libraries across six provinces. Subjects – 21 academic librarians across a range of professional roles. Methods – Researchers conducted online Zoom interviews, firstly through pilots at two institutions before broadening to any Canadian higher education library, which were then transcribed. The 4-D cycle of appreciative inquiry, a strengths-based approach to change, was used to guide the development of the generative interview questions. The data analysis of the transcripts was underpinned by hermeneutic phenomenology, with interpretations using meaning assigned by participants themselves, and utilized thematic analysis with open coding and constant comparison. Main Results –The authors identified eleven factors under three main categorizations of learning-specific factors, structural factors, and internal factors, which participants attributed to conditions that shape ideal learning experiences. The researchers identified four key insights as a result of their research that added to previous literature on this topic; the importance of personal identity and positionality in shaping learning experiences, the importance of seeing learning in the context of accumulated learning journeys rather than single activities, the dynamics of different types of learning including informal learning and those beyond professional contexts, and lastly that academic institutions themselves shape individuals learning experiences. Conclusion – The authors identified key factors that shape the EDI learning experiences of Canadian academic librarians and shared their learning experiences, which can motivate other groups of librarians to reflect on their own EDI learning journeys and motivations.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".