Biting Off More Than You Can <i>Chew</i> at Work: Measuring Individual Perceptions of Cultural Hard and Excessive Work (I-CHEW)
Bibliographic record
Abstract
Previous research attributes differences in working styles (e.g., diligence, excessive hours) primarily to individual traits or values, such as workaholism, neglecting cultural context. This research introduces cultural work ideals—subjective perceptions of societal expectations about work—and distinguishes between perceived cultural values of (a) hard work (i.e., efficiency, high quality, wise time use) and (b) excessive work (i.e., long hours, high quantity, constant work prioritization). We develop and validate the I ndividual Perceptions of C ultural H ard and E xcessive W ork (I-CHEW) Scale across six diverse North American samples ( N = 1,902), including full-time employees, business undergraduates, MBA students, and alumni. Psychometric analyses support the I-CHEW’s reliability and validity. As hypothesized, perceiving a cultural ideal of hard work predicts beneficial outcomes (e.g., lower cynicism, higher engagement) beyond individual, organizational, and cultural factors. Conversely, perceiving a cultural ideal of excessive work predicts negative outcomes, including greater emotional exhaustion, reduced well-being and job satisfaction, and poorer physical health.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".