Bibliographic record
Abstract
Purpose This paper aims to overview the professional context for Canadian research libraries (as outlined in the 8Rs Canadian Library Human Resources Study by Ingles et al.) and to examine the approach and response to dynamic human resources challenges and opportunities unfolding through a strategic planning and change management process at the University of Saskatchewan (U of S) Library. Design/methodology/approach The paper discusses the context and challenges for Canadian research libraries as highlighted in the 8Rs Study and overviews this in the context of the U of S Library's response through its strategic planning and change management process. It explores institutional responses and the possibilities of joint collaborative action across member libraries of the Canadian Association of Research Libraries (CARL). Findings The study finds that greatest challenge to transforming library services, resources and facilities lies in transforming the knowledge, skills and abilities of library staff and to developing new models and approaches to professional practice, which meet and exceed client expectations and overcome the traditionally conservative approach to the practice of librarianship. Originality/value The paper provides a discussion on strategic options and strategies for research libraries as exemplified by the experiences and work underway at the U of S Library. While some of the context is Canadian specific, the U of S response contains many strategies applicable in other academic and research libraries contexts.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".