An Analysis of the Effects on Parties' Unionization Decisions of the Choice of Union Representation Procedure: The Strategic Dynamic Certification Model
Bibliographic record
Abstract
This article proposes a new theoretical framework-the strategic dynamic certification model-to explain how union certification processes operate. Statutory certification procedures are not neutral. Instead, they produce particular incentives, disincentives, and opportunities for employers, unions, and employees, and these affect the outcomes of the procedure. Empirical evidence confirms this model's ability to analyze the certification process and the outcomes of unionization attempts. In particular, this model explains why the change from a card-check to a mandatory representation vote encourages unlawful employer conduct, enhances the effectiveness of union avoidance activities, and deters employee participation in the unionization decision. The article concludes that, in order to accommodate a mandatory vote procedure within the existing labour relations framework, it is necessary to counteract employer unfair labour practices during union organizing through modifications to the certification procedure. Finally, the strategic dynamic certification model is applied to develop specific proposals for legislative reform aimed at reducing the negative effects of the mandatory vote procedure, thereby enhancing the validity and credibility of the certification process and of the labour relations system.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".