The Job Satisfaction Paradox: Pluralistic Ignorance and the Myth of the “Unhappy Worker”
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".