Understanding the dynamics of inequity in collective bargaining: evidence from Australia, Canada, Denmark and France
Bibliographic record
Abstract
Unions and collective bargaining are generally considered to be the main vehicles for ensuring equity at work. This article questions this assertion by examining distinct forms of inequity between workers in unionised workplaces and, more specifically, the role of collective bargaining in creating, maintaining, reducing or avoiding them. Based on a study conducted in Australia, Canada (Québec), Denmark and France, the situations of inequity examined are related to employment and working conditions, and favour one group of workers over another group of workers performing the same tasks in the same workplace. To better apprehend these dynamics and distinguish between different situations, we develop an analytical framework to capture them. Then, we focus on one example observable in each country: two examples of inequity based on date of hiring (Canada and Australia) and two based on employment status (France and Denmark), showing how the four ideal-type processes interact in each national context. Based on an analysis of these examples, we demonstrate the segmentation between core and non-core employees, along the lines of segmentation theory, but also within groups of insiders or core employees and the key factors that explain how the collective bargaining process can lead to inequity: time, balance of power, and workplace institutions.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".