Academic Librarians and Research: A Study of Canadian Library Administrator Perspectives
Bibliographic record
Abstract
Within the literature exploring the role of research in academic librarianship, very little attention has been paid to the perspectives of upper library administrators. This perspective is critical because library administrators play a key role in hiring, evaluating, supporting, promoting, and tenuring professional librarians. As a way of bringing the administrative perspective to these discussions, our study examines how library administrators within the Canadian Association of Research Libraries (CARL) view the role of research in their own libraries and within academic librarianship, as well as how they perceive the current and future climate for librarians’ research. Our study reveals key areas in need of further research and identifies several issues that librarians and upper administrators would benefit from exploring together to advance discussions about research.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.087 | 0.029 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".