The scarce evidence behind hybrid and telework policies in government
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it