Bibliographic record
Abstract
The COVID-19 pandemic has significantly transformed work arrangements and reshaped employees' expectations of their employers. In Canada's public sector, hybrid work, where employees alternate between office and home, has become the new norm. However, the fairness and equity of existing hybrid work arrangements remain understudied. For instance, in 2022, the Ontario Public Service (OPS) mandated three office days per week for all employees, but the latest OPS People Plan (2023-2026) lacks concrete commitments to hybrid work. This paper explores public servants' perspectives on the fairness and equity of hybrid work arrangements within the OPS. Specifically, it examines public servants' views on access to hybrid work arrangements, consistency in their implementation, and the impact of hybrid work on recruitment and retention. By analyzing Reddit posts from 627 OPS subreddit users collected in 2022 and 2023, the study addresses two research questions: 1) How do Ontario public servants interpret fairness and equity in the current hybrid work model? and 2) How can these interpretations inform potential changes in the Ontario Government’s approach to hybrid work? The findings reveal vivid discussions on hybrid work arrangements, highlighting concerns with the existing approach as well as the operationalization and implementation of the hybrid work. From a fairness and equity perspective, several key aspects emerged: the uneven burden of hybrid work, lack of consistency in its implementation and enforcement, and the impacts on employee recruitment and retention. The study concludes that significant changes are needed in the OPS approach to hybrid work, starting with a thorough analysis and subsequent incorporation of public servants' perspectives on fairness and equity. This could determine if the Ontario government remains competitive in the digital age.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".