The effects of the socio‐demographic factors on judgement building in arbitration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it