What Shapes Academic Librarians’ Teaching Practices? A Holistic Study of Individual Librarians, their Contexts, and their Professional Learning Activities
Bibliographic record
Abstract
For academic librarians, teaching is often a core part of their work, but they typically receive limited preparation for this role in their professional education. The literature on librarians’ development as teachers generally stresses their professional development opportunities. This qualitative study explored the range of experiences, professional contexts, and professional learning activities that shape 12 academic librarians’ teaching practices. As both librarian and researcher, I was positioned as an insider-outsider. I took a bricolage approach that combined narrative, case study, and analysis based on an a priori model that situated the individual librarians in their local, professional, higher education, and societal contexts. I recruited 12 teaching librarians from Canada and the United States and conducted two semi-structured interviews with each. I stitched together a composite picture of the participants’ unique, varying experiences, with rich examples of the ways they understood and developed their teaching roles in relationship to their varied contexts, and in relationship to their own values (e.g., a commitment to social justice; an ethic of care), identities, and prior experiences. I identified two stages in their learning: 1) an initial “learning to teach as a librarian” stage, in which they required (but did not always receive) greater support, and 2) lifelong learning. Librarians in both stages were strongly self-directed and employed a variety of strategies. The professional context served as a broad community of practice that supported their development of a teacher identity and provided them with norms and guidelines. The individuals and their contexts varied enough, however, that no single picture of librarians’ teaching could emerge. This holistic approach to studying librarians’ development as teachers suggests new approaches for practice as well as new questions for research. In particular, the initial “learning to teach as a librarian” stage demands more attention. More broadly, studying and valuing the librarians’ experiences provides important and often absent perspectives on the work of academic librarians in the United States and Canada. It positions librarians as practitioners with important emic perspectives that enable them to generate knowledge that is both local and public.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".