Bibliographic record
Abstract
A growing literature indicates that people are increasingly motivated to experience a sense of meaning in their work lives. Little is known, however, about how perceptions of work meaningfulness influence job choice decisions. Although much of the research on job choice has focused on the importance of financial compensation, the subjective meanings attached to a job should also play a role. The current set of studies explored the hypothesis that people are willing to accept lower salaries for more meaningful work. In Study 1, participants reported lower minimum acceptable salaries when comparing jobs that they considered to be personally meaningful with those that they considered to be meaningless. In Study 2, an experimental enhancement of a job's apparent meaningfulness lowered the minimum acceptable salary that participants required for the position. In two large-scale cross-national samples of full-time employees in 2005 and 2015, Study 3 found that participants who experienced more meaningful work lives were more likely to turn down higher-paying job offers elsewhere. The strength of this effect also increased significantly over this time period. Study 4 replicated these findings in an online sample, such that participants who reported having more meaningful work were less willing to leave their current jobs and organizations for higher paying opportunities. These patterns of results remained significant when controlling for demographic factors and differences in job characteristics.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".