University Policies on Employee Remote/Hybrid Work: Would Converging Policies Entail Any Sustainable Change in Institutional Operations?
Bibliographic record
Abstract
As higher education institutions (HEIs) address post-pandemic working modalities by publishing telework policy guidelines for employees, a research gap appears in understanding the content and impact of such policies. This chapter employs document analysis to identify prevalent themes in telework policies across 15 US and Canadian HEIs. Examining the study through the lens of organizational theories, the authors demonstrate that institutions develop isomorphically converging telework policies characterized by intentionally or unintentionally vague language, which exacerbates existing states of organized anarchy. This approach could lead to misalignment with employee needs and negatively impact organizational justice. To prevent employee disengagement and subsequent loss of talent, leadership must recognize the need for further evaluation and improvement of current policies. By also referencing secondary data from the CUPA-HR employee retention survey, the authors identify employee populations that are particularly vulnerable to the impacts of current telework policies. Ultimately, the authors propose the development of more equitable telework policies with consideration of the dimensions of procedural, interactional, and distributive justice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".