Democracy, impartiality and the online political activity of Aotearoa New Zealand’s public sector employees: similarities and differences with other Westminster countries
Bibliographic record
Abstract
While some hail social media as improving political participation, some governments have received social media with a touch of trepidation; concerned that public servants’ online political activity might threaten the public service’s reputed impartiality. Recent research from some Westminster countries where government messaging about social media have been especially cautious have found a negative relationship between public sector employment and online political activity. But what about public sector employees in Aotearoa New Zealand, where, comparative research suggests, the tone and substance of social media guidelines are less risk-averse than other Westminster countries? Using data from the 2014, 2017 and 2020 New Zealand Election Study, this article examines the relationship between public sector employment and online activity with several multivariate regression models. The results lead to two conclusions. First, consistent with research from other countries, a negative relationship has emerged over time between public sector employment and online political activity in Aotearoa New Zealand. Second, although public sector employees are less politically active online relative to other citizens, the substantive size of this gap is not as great as that found in other Westminster countries. The implications of these findings for the state of democracy and impartiality in Aotearoa New Zealand are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".