Public servants’ political activity online in an institutional environment of caution: the role of personality traits
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
Purpose As social media has become an ingrained aspect of our lives—including our political relationships with other citizens and the state—various governments have warned public servants that being politically active online might threaten the reputed impartiality of themselves and the public service. This study examines whether public servants are less likely to be politically active on social media than other citizens, and seeks to understand public servants’ varying disposition to be politically active online by investigating the role of employees’ underlying Big 5 personality traits. Design/methodology/approach Multivariate regression, along with marginal effects and predicted probabilities, are used to investigate public servants’ online political activity with survey data from Canada, a country where impartiality is a core public service value, and where governments, public service commissions and even public sector unions have voiced cautious messages about the threat online political activity presents to the reputed impartiality of public servants, and the public service at large. Findings Analysis of the direct effects of being a public servant and each Big 5 personality trait finds that being a public servant significantly, and substantively, reduces the probability of engaging in online political activity, meanwhile, Extraversion and Conscientiousness have consistent, significant and substantive relationships with being politically active online. Subsequent analysis investigating the dynamic between the Big 5 and being a public servant, uncovers a more complex story. Among public servants, Openness and Neuroticism, rather than Extraversion and Conscientiousness, are associated with significant and substantive changes in the probability of engaging in some online politically activities. This is consistent with research investigating the relationship between the Big 5 and risk aversion, given that public servants in Canada work in an environment with a highly cautious discourse portraying social media as a serious risk to impartiality. Practical implications The findings also speak to best practices for public service human resource managers by shedding light how public servants’ behavior can be better understood and managed by paying attention to their underlying personality traits. Originality/value This study moves beyond analyzing trends between public and private sector employees, to instead examine public servants’ online political activity. This study offers theoretical and empirical insight into how public servants’ disposition to be politically active online is, in part, influenced by their underlying Big 5 personality traits, specifically, Neuroticism and Openness.
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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 | 0.001 | 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 | 0.000 | 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