Lazy Bureaucrats? Studying stereotypes of civil servants across countries
Bibliographic record
Abstract
This study examines whether a universal public sector stereotype exists by investigating similarities and differences cross-nationally and by different types of jobs. Dedicated public sector workers are crucial for a well-functioning society. We all depend on them for our safety, health and mobility. However, public sector workers are constantly portrayed as lazy, incompetent and even evil (Goodsell, 2004). In Western countries, the expression of negative stereotypes is even quite commonly accepted. These stereotypes, however, could negatively affect public sector workers in their performance work and wellbeing (Spencer et al., 2016). Furthermore, it is not clear to what extent these negative stereotypes are shared among citizens across different countries nor whether they differ by type of worker, such as police officers or judges. This study answers the following research questions: - Which stereotypes exist among citizens about public sector workers? - How do these stereotypes differ between different public sector occupations? (Public sector workers generally, police officers, judges, and tax officials. Private sector workers will be used as a reference group.) - How do these stereotypes differ between countries with markedly different administrative traditions (South Korea, Canada, the United States, and the Netherlands)?
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".