The effects of the socio‐demographic factors on judgement building in arbitration
Bibliographic record
Abstract
Abstract This study examines how the socio‐demographic characteristics of arbitrators and of plaintiffs affect arbitrators' judgement bases for arbitration decisions. Two research questions are tested quantitatively based on a data set of arbitration decisions in the Canadian university sector collected from the website of the Canadian Legal Information Institute. We created two models of independent variables related to the socio‐demographic characteristics of arbitrators and plaintiffs. Multinomial logistic regression is used to examine the possible impacts of these variables on the justifications used by arbitrators to explain their decisions. The results indicate that both models significantly influence how arbitrators justify their arbitral decisions. The following variables significantly contribute to the models: arbitrator's age, arbitrator's professional experience in management, plaintiff's gender, and support of the plaintiff by a collective entity (union or association). Young arbitrators are more likely to use “laws” and those who have professional experience in management tend to cite “evidence” to justify their arbitral decisions. Also, arbitrators are more likely to use “evidence” as their judgement basis for male plaintiffs who are supported by a collective entity. The details of these findings, limitations of the study, and future directions for research are further discussed.
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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 | 0.013 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".