The dynamics of strike votes: Perceived justice during collective bargaining
Bibliographic record
Abstract
Summary Why do workers decide to go on strike or change their mind during a strike? This field study aims to determine to what extent employees' perceptions of justice formed during the collective bargaining process influence their strike vote. Data were collected from a North American university faculty that went on strike 21 months after the expiration of its collective agreement. The results show that perceived justice about collective bargaining is a determinant of the strike vote. The role played by the employer and the union as a source of (in)justice and the importance of the four types of justice perceptions (procedural, distributive, interpersonal, and informational) vary depending on the context of ballots (initiate, continue, or end the strike). This suggests that the reduction of uncertainty leads to the activation of three different mechanisms: judgmental heuristics (salience), social comparison, and cognitive dissonance. The results also suggest that employees attempt to be as rational as possible when they decide to go on strike. Nevertheless, the decision to join strikers and to continue the strike is partly based on an emotional process: employees use the strike to punish the employer. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.016 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".