The effect of expedited, tripartite, and conventional arbitration on arbitration outcomes
Bibliographic record
Abstract
Grievance arbitration is well established in Canada. All ten provincial jurisdictions, as well as the federal jurisdiction, require that every collective agreement shall provide for the final and binding settlement, by arbitration or otherwise, of all differences between the parties arising from the collective agreement. Notwithstanding its universality, grievance arbitration has been the subject of criticism, including: lack of timeliness, increasing cost, and excessive legalism. Expedited arbitration has been proposed as a method to address these criticisms. Expedited arbitration has no fixed definition. It is a general reference to any set of procedures or mechanisms designed to expedite the conventional arbitration process. Expedited arbitration has been successful in addressing the criticisms of conventional arbitration. However, it too has been criticized. There have been suggestions that expedited arbitration awards are inferior to conventional arbitration awards. This is due to the summary nature of its procedures, and due to the fact that many new arbitrators use expedited arbitration appointments as a method to gain experience. The purpose of this study is to investigate whether arbitration outcomes differ depending upon the type of arbitration used: expedited arbitration with a single arbitrator, conventional arbitration with a single arbitrator, or conventional arbitration with a tripartite board. Using logistic regression, 664 discipline and discharge arbitration awards, dated in 1994 and 1995 and filed with the Office of Arbitration at the Ontario Ministry of Labour, were analyzed. Evaluated at the mean probability of a grievance being upheld (0.533), and relative to the reference category of conventional arbitration with a single arbitrator, expedited arbitration with a single arbitrator is associated with a 0.195 (or 19.5 percentage point) increase in the probability of a favourable arbitration decision for the grievor, holding all other factors constant. The result is significant at the 0.05 level. Conventional arbitration with a tripartite board is associated with a 0.068 (or 6.8 percentage point) increase in the probability of a favourable arbitration decision for the grievor. However, this result is not statistically significant.
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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 | 0.080 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".