The scarce evidence behind hybrid and telework policies in government
Bibliographic record
Abstract
The global pandemic forced all government bureaucracies to shift rapidly and nearly exclusively to remote work, yet governments post-pandemic opted for various work mode paths, from a full return to in-person work, hybrid, or fully embracing remote. This research seeks to answer: What information did public organizations rely on to assess the productivity of telework when formulating telework and hybrid work policies for their workforce? To what extent are digital work surveillance tools used? We examined conditions across departments in Canada’s federal and provincial governments, as revealed by 166 Access to Information and Privacy (ATIP) requests. Our findings indicate that only 14.3% of the sampled Canadian departments conducted thorough analyses of employee productivity, effectiveness, efficiency, or equity with telework prior to implementing their post-pandemic telework policies. Additionally, around 10% of Canadian departments utilized some form of digital surveillance tools on their employees. We did not find a relationship between departments that conducted comprehensive evaluations of remote and hybrid work effectiveness, efficiency, and equity, and their use of digital work surveillance. Taken together, we find little evidence that telework and hybrid work policies have been devised through an evidence-based approach.
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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 | 0.022 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".