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The Faces of Bureaucracy: A multi-method study of civil servant stereotypes and their consequences

2025· dissertation· en· W7149214400 sur OpenAlexaboutno aff
Isa Bertram, Bestuur en Beleid, UU LEG Research USG Public Matters, Publiek Management en Gedrag, Lars Tummers, Robin Bouwman

Notice bibliographique

RevueUtrecht University Repository (Utrecht University) · 2025
Typedissertation
Langueen
DomaineSocial Sciences
ThématiquePublic Policy and Administration Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCivil servantsCivil servantAffect (linguistics)Civil serviceSocioeconomic statusJob satisfactionSurvey data collectionWork (physics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Prejudices about civil servants have been prevalent for centuries, with civil servants often the subject of negative stereotypes and jokes. In her dissertation The Faces of Bureaucracy, Isa Bertram explores these stereotypes and their impact on public service through four empirical studies. The first study offers an international comparison, surveying citizens in the Netherlands, Canada, South Korea, and the United States. The findings revealed that stereotypes about civil servants vary by region. In North America, stereotypes were mostly positive, with civil servants viewed as hardworking, helpful, and responsible. In contrast, in the Netherlands and South Korea, stereotypes were more negative, with civil servants seen as inflexible, boring (Netherlands), or even corrupt (South Korea). Bertram’s second study examines whether stereotypes differ across socioeconomic status. She found that people with lower income levels generally held more negative views of civil servants than those with higher income levels. However, the differences were more about the types of traits associated with civil servants. Lower-income individuals were more likely to view civil servants as strict and arrogant, while higher-income individuals focused more on work-related traits, such as leaving work early. The third study investigates how stereotypes affect citizens’ experiences with public services. Results of this survey experiment indicated a confirmation bias effect of the stereotypes, where citizens’ expectations based on stereotypes shaped their experiences. Participants with negative stereotypes activated tended to report lower satisfaction and poor experiences with public services, while those with positive stereotypes activated had more favorable experiences. These findings contrast with the expectation-disconfirmation model, which is commonly used to assess satisfaction with public services. In her final study, Bertram interviewed civil servants to understand how they perceive these stereotypes and cope with them. Respondents generally didn’t view stereotypes as a problem for their personal wellbeing – they were concerned about the impact on public service and the relationship between citizens and the government, for instance regarding trust. In addition, respondents used different perspectives to make sense of the stereotypes: Some saw them as based in truth, while others viewed them as an inevitable consequence of the complex nature of government work. These differing perspectives helped civil servants cope with the negativity, offering a form of self-protection – however, this form of coping can also lead to blind spots, for instance in interpreting critical citizen feedback. Taken together, Bertram’s research illustrates that civil servant stereotypes are multifaceted and closely intertwined with other related concepts, such as trust in government and perceptions of public organizations. In addition, the research underscores the importance of considering the contextual reality of public administration and public services when studying the consequences of civil servant stereotypes. In sum, the research highlights that in studying civil servant stereotypes, we can benefit from a nuanced approach to understanding what they are and what they represent: In part, overgeneralized misconceptions, but also reflections of misunderstandings between citizens and the public sector, justified criticisms of public services, or even truths about bureaucratic tendencies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,293
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,328
Écart entre enseignants0,291 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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