Public servants, anonymity, and political activity online: bureaucratic neutrality in peril?
Bibliographic record
Abstract
Various actors have recently expressed concern that by threatening anonymity, social media places the bureaucracy’s neutrality in jeopardy. Yet, empirically, little is known about the online political activities of public servants. Drawing upon the public service motivation literature, this article develops contrasting hypotheses between public sector employment and online political activity. Testing hypotheses with survey data from Canada, the results show that unionized public sector employment reduces the probability of being politically active online. As social media continues to change the nature of governance, the results suggest that anonymity and neutrality remain important professional norms within the Westminster administrative tradition, and are reflected in the online political activities of public sector employees in Canada.Points for practitioners• Due to its visibility and permanency, public servants’ political activity on social media potentially threatens their reputation as politically impartial officials.• Some governments and public sector unions have thus voiced messages of caution to administrative personnel about the dangers of being politically active online.• Survey data from Canada suggest that these messages have worked.• Unionized public sector employment reduces the probability of being politically active online but does not reduce the probability of being active in traditional “offline” political activities.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".