Bibliographic record
Abstract
In this dissertation, I delve into the intricate realm of public sector worker stereotypes, by investigating what are public sector worker stereotypes, their contributing factors, and their impact on citizen-state interactions. It is structured around three main research questions and supported by empirical evidence from surveys and experiments. The first question attempts to unveil the spectrum of stereotypes that citizens hold about public sector workers across four countries – Canada, the Netherlands, South Korea, and the U.S. Surprisingly, alongside negative stereotypes, such as lazy, boring and corrupt, positive ones like being hardworking and responsible are also prevalent. Three stereotypes are shared cross-nationally (namely: going home on time, job security, and serving) but the perception whether they are positive or negative and most of their content differ, suggesting nuanced perceptions shaped by country contexts. The second question explores factors contributing to these stereotypes, demonstrating the influence of media portrayals, trust levels, and geographic and educational backgrounds. Positive media coverage enhances positive stereotypes, while negative media coverage contributes to negative stereotypes. Trust and education aggregated with geography patterns affect also perceptions of public sector workers. For instance, rural non-college-educated individuals tend to view police more positively than urban college-educated individuals. High trust towards a profession is associated with more positive stereotypes of the profession, while low trust is associated with more negative stereotypes. The third question investigates the effects of stereotypes on citizen-state interactions. I investigated whether (1) confronting public sector workers with positive stereotypes about their profession affects their interactions with citizens, and (2) confronting citizens with negative stereotypes of public sector workers affects citizens’ behavior towards public employees. Reminding public sector workers of positive stereotypes associated with their profession, as demonstrated through a field experiment, contributes to friendlier interactions towards citizens during public service delivery. However, negative stereotypes, like bureaucrat bashing, do not significantly affect citizen behavior, as demonstrated through a survey experiment. An additional find was that if public sector workers share their vulnerabilities at work, citizens act more compassionately towards them. I draw two important conclusions: There is power in positive stereotypes. They can improve citizen-state interactions, such as by influencing more positive interactions during public service delivery. They can also potentially attract talent to the public sector. This may be especially relevant in the areas that countries face a staff shortage crisis in certain public professions, such as elementary school teachers in the Netherlands, or healthcare workers and police officers in the U.S. Narratives about public employees have an impact on stereotyping and citizen-state interactions. Media coverage can contribute to shaping stereotypes, while authentic storytelling from public sector workers can humanize their experiences, fostering understanding and compassion among citizens. Authentic story telling can be, for instance, public employees such as social workers sharing with media the complex cases they face, and the time and budgetary constraints they have in dealing with those cases, such as working unpaid overtime to help families or working with children with traumatic childhoods and the constraints you face as a social worker.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".