The Good, the Bad, and the Bureaucrat: Investigating positive and negative public sector worker stereotypes
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».