Exploring Reddit discussions of remote work and return to office in the Canadian federal public service
Bibliographic record
Abstract
Recently, federal and organizational policies, such as those outlined by the Canadian Federal Public Service, have mandated that employees transition from their full-time remote work (RW) setup and return to the office (RTO). However, little empirical research has been conducted to examine how these employees are experiencing the mandated RTO transition. We aimed to explore the adjustment process drawing from the theory of work adjustment. Using semi-automated web-scraping technology, eight months (June 2022 to January 2023) of publicly available comments on a Reddit subgroup focused on the Canadian Federal Public Service were gathered. Overall, 20 048 comments were used. Thematic analysis revealed varied perceptions towards RW and RTO. Additionally, eight thematic categorizations were proposed: Ability-Environment Congruence, Need-Supply Fit, Feedback and Adaptation, Person-Environment Fit, Psychological Health, Work Context and Organizational Culture, Perceived Impact on Personal Life, and Environmental Concerns. Findings suggest that an employee-centered perspective may be beneficial when implementing change initiatives such as a mass RTO. This study provides a basis for future research and informed federal decision-making, identifying salient commenter perspectives that could be used when formulating policies and practices concerning RW and RTO within the Canadian Federal Public Service.
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.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".