To vote or not to vote: abstaining from voting in union representation elections
Bibliographic record
Abstract
Abstract We conducted two studies addressing abstaining from voting in union representation elections. In Study 1 of a faculty representation election, we showed that compared to voters abstainers possessed less extreme work and union attitudes, believed less in the ability of their vote to affect the election outcomes, and were less involved in the election (e.g., less interested, felt less responsibility to vote). To assess the practical utility of these findings, Study 2 used vignettes in a 2 (traditional bread‐and‐butter issues) × 2 (emerging issues related to fairness) × 2 (voting instrumentality) × 2 (responsibility to vote) experimental manipulation. Results showed that the likelihood of abstaining is reduced when efforts to emphasize the responsibility to vote are presented together with information on both traditional and emerging issues. The two studies show why people abstain from voting in union representation elections, and suggest how abstaining might be reduced. Conceptual implications, practical interventions and research directions raised by the two studies are discussed. Copyright © 2001 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".