Bibliographic record
Abstract
Libraries around the world are exploring new models for operating and providing services post-pandemic. Many case studies have been written about individual programs and services, alongside articles about libraries pivoting as a response to COVID-19; however, this study is a large-scale, national study on library work. As academic libraries have been playing with flexible schedules and different types of workspaces, there are major implications for how librarians work and their level of satisfaction with their jobs. This study of English-speaking Canadian academic librarians is the first national overview of how and where librarians across the country are working, and levels of satisfaction with different working situations and conditions. The following research questions were considered: 1. What are librarian work schedules, and are they able to choose their schedules? 2. What kinds of workspaces do they have? 3. What do they like about their workspaces? 4. What do they feel can be improved with their schedules and workspaces? Results show that the majority of academic librarians work in closed offices, and prefer this set up. Most work on campus between 3 and 5 days a week, and choose which days they work from home . This study fills a gap in literature on the current working environment in academic libraries , providing comprehensive findings on how librarians feel about their working conditions and schedules. Results are easily adaptable to other library settings and to other academic units.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.017 |
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".