What Accounts for the Representation Gap? Decomposing Canada—US Differences in the Desire for Collective Voice
Bibliographic record
Abstract
We utilize two representative cross-national data sets to shed light on what has been a vexing problem in the industrial relations literature; namely, the existence and persistence of the representation gap documented more than a decade ago by Freeman and Rogers (1999). Specifically, we estimate the determinants of employee desire for a range of collective voice mechanisms, including unionization. We do this separately for the US and Canada and then, using an application of the Oaxaca decomposition technique, we decompose the differences in those desires between the two countries into a component due to differences in the characteristics of respondents and another due to differences in preferences for collective voice mechanisms. Our results indicate that: (1) roughly half of workers in both countries expressed a desire for a range of collective voice mechanisms to deal with workplace issues; (2) that desire for collective voice was stronger in the US than in Canada; and (3) that virtually all of the stronger desire for collective workplace voice in the US, as compared to Canada, was due to stronger employee preferences for collective solutions as opposed to differences in the characteristics of workers. We offer plausible explanations for our findings and discuss the implications for labour law reform.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".