The Faces of Bureaucracy: A multi-method study of civil servant stereotypes and their consequences
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".