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Record W4399103135 · doi:10.1177/01902725241253252

The Job Satisfaction Paradox: Pluralistic Ignorance and the Myth of the “Unhappy Worker”

2024· article· en· W4399103135 on OpenAlexafffund
Paul Glavin, Scott Schieman

Bibliographic record

VenueSocial Psychology Quarterly · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of TorontoMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsJob satisfactionSocial psychologyMythologyPsychologyIgnoranceJob attitudeSociologyJob performanceEpistemologyPhilosophyTheology

Abstract

fetched live from OpenAlex

American media coverage of the “Great Resignation” may have contributed to a belief that job dissatisfaction is widespread in the United States, even though surveys show relatively high and stable levels of job satisfaction among American workers. Using data from the 2023 Quality of Employment Survey, we investigate whether individuals’ beliefs about job dissatisfaction mirror empirical evidence or align more with media portrayals of widespread discontent. While most study participants expressed personal job satisfaction, over half believed that the majority of Americans were not at all satisfied, indicative of pluralistic ignorance—a phenomenon involving a collective misperception about a group’s norms or beliefs. Dissatisfaction beliefs were more common among remote workers and those with fewer work friendships. Moreover, believing in widespread job dissatisfaction was associated with lower organizational commitment, controlling for personal job satisfaction. We discuss the role of pluralistic ignorance in reconciling personal experiences with contrasting media representations of work and the economy.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.426
Teacher spread0.390 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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