Understanding public servants’ perspectives on return to office and digital government: lessons learned from Canada
Bibliographic record
Abstract
Purpose The purpose of this study is to explore public servants’ perspectives on return to office, hybrid work model and digital government. This is done by answering two research questions: what benefits and challenges public servants foresee with return to office and whether hybrid work enables or impedes digital government initiatives. Design/methodology/approach This research is designed as a mixed methods study of federal and provincial governments in Canada based on the analysis of Reddit data. Research methods include machine-assisted toxicity and sentiment analysis and manual content analysis to identify emerging themes. Findings The findings highlight public servants’ mostly discussed concerns with return to office. Other notable discussion topics include resistance to the hybrid work model and identifying the ways how it would be operationalized. Some supported return to office. Research limitations/implications This study’s limitations are related to using Reddit as the data source and user representation on Reddit. The main implications are its contribution to emerging literature on the future of work and digital government. Practical implications This study highlights that perspectives of public servants are paramount for development and implementation of transformational initiatives and offers insights for public sector managers on how to incorporate these into practice while improving the efficiency of digital government initiatives and the system. Originality/value This study addresses the gap in literature by seeking to understand the perspectives of public servants in a variety of roles as well as implications of transition to hybrid work on digital government and future of work initiatives.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".