Supporting the Academic Library Workforce: Updates from the Canadian Association of Research Libraries
Bibliographic record
Abstract
The Canadian Association of Research Libraries (CARL) has invested significant energy over the last 20 years in building workforce capacity across the country’s academic libraries. The global pandemic has given this work a whole new intensity as directors find themselves seeking to fill large numbers of vacancies in a highly competitive market, to encourage more candidates from equity-deserving groups to apply, to prepare both new and long-serving staff members to take on new kinds of work, and to create workplace environments that encourage staff to stay. This paper will update the international community on a number of CARL initiatives that are collectively building the national understanding and approach to library workforce capacity. Most notably, the paper will introduce the new Competencies for Librarians in Canadian Research Libraries (September 2020) which aims to help with both personal and organizational goal setting, recruitment and professional development. The paper will also describe the national Diversity and Inclusion Survey conducted in partnership with the Canadian Centre for Diversity and Inclusion (CCDI) to better understand both the demographics of the CARL workforce and how individuals from various equity-deserving groups experience that workplace. The paper will document the dramatic increase in the number and engagement in professional learning and development opportunities for staff in Canadian research libraries, from webinars and symposiums to community calls and cross-Canada coffee chats. Finally, the paper will demonstrate the use of a logic model as envisioned by CARL’s Library Impact Framework Working Group, as a strategy for an individual library to visualize the impact of its various efforts on welcoming and retaining new staff.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".