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Record W4386805442 · doi:10.1080/14719037.2023.2254306

Working 9 to 5? A cross-national analysis of public sector worker stereotypes

2023· article· en· W4386805442 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePublic Management Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsÉcole Nationale d'Administration Publique
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekNational Research FoundationNational Research Foundation of KoreaUniversity of Oxford
KeywordsPublic sectorStereotype (UML)Context (archaeology)Political scienceJob securityPerceptionDemographic economicsPublic relationsSociologySocial psychologyPsychologyEconomicsGeography

Abstract

fetched live from OpenAlex

We present an inductive, citizen-driven appraoch to identify stereotypes of public sector worekrs across the United States, Canada, the Netherlands and South Korea (Study 1: n=918; Study 2: n=3,042). Contrary to common negative portrayals, we idetify two positive stereotypes across countries — having job security and serving society; and one neutral/negative stereotype — going home on time. Notably, Americans and Canadians have a more favorable view of public sector workers than the Dutch and South Koreans. This study opens avenues for exploring positive public sector stereotypes and the impact of context on these perceptions.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.009
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.180
GPT teacher head0.438
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it