Flexible Work Agreements: Here to Stay but Uneven in Equity and Promoting Success
Bibliographic record
Abstract
A Review of:Hosoi, M., Reiter, L., & Zabel, D. (2021). Reshaping Perspectives on Flexible Work: The Impact of COVID-19 on Academic Library Management. portal: Libraries and the Academy 21(4), 695-713. doi:10.1353/pla.2021.0038 Objective – The article seeks to assess the current state and the future of flexible work agreements (FWAs) in research libraries. Design – The authors held semi-structured interviews with 31 individuals in library leadership roles. Setting – Large American or Canadian research libraries during the COVID-19 pandemic. Subjects – 31 individuals in senior leadership roles (ex: associate dean, director) at the top 50 research libraries in North America (based on the Association of Research Libraries Investment Index). Methods – Interviews were conducted and recorded over Zoom with participant, investigator, and note taker. Investigators developed a quantitative coding instrument based on a selection of the interviews, then coded all interviews independently. Coded data were evaluated for broader themes in a collaborative fashion. Main Results – All participants had employees working partially or fully remotely at the time of the interviews. Half of participants observed gains in productivity during the pandemic, although even more commented on technology challenges. Other positives included remote project success and more inclusive meetings; other negatives included caregiving and job duties that did not allow for remote work. Conclusion – While FWAs were widely available pre-pandemic, they were not normative. The majority of participants think flexible work will only increase in libraries and will influence recruitment and retention of employees, as well as utilization of library space.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.161 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".