Bibliographic record
Abstract
In a separate, related paper, we reveal and criticize an important “methodical error” which is commonly made in labour law jurisprudence. In this paper, we undertake a review of the attempts by Canadian and UK legislators and decision makers to overcome some of the problems of applying labour law’s traditional orthodoxy in the face of an ever more complex world by creating new categories “in between” the existing classes of employees and independent contractors. These attempts often fail precisely because they perpetuate the traditional approach and its methodological error, maintaining its perverse incentive structures. Our advice: “Don’t mess with Mr. In-Between”. Dans un article distinct, mais connexe, nous révélons et critiquons une « erreur méthodologique » importante qui est couramment commise dans la jurisprudence du droit du travail. Dans le présent article, nous examinons les efforts déployés par les législateurs et les décideurs canadiens et britanniques pour surmonter certains des problèmes liés à l’application de l’orthodoxie traditionnelle du droit du travail dans un monde de plus en plus complexe, en créant de nouvelles catégories « intermédiaires » entre les catégories existantes d’employés et d’entrepreneurs indépendants. Ces tentatives échouent souvent précisément parce qu’elles perpétuent l’approche traditionnelle et son erreur méthodologique, en maintenant ses structures perverses d’incitation. Notre conseil : ne vous asseyez pas entre deux chaises!
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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 | 0.028 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".