Bibliographic record
Abstract
One of the clear examples for the ability of academics to influence the law, and the ability of law to influence the lives of workers, stems from Harry Arthurs' contribution concerning dependent contractors – an intermediate category between “employees” and independent contractors. The binary divide between the two traditional categories has crucial importance for people who work for others: either they enjoy the protection of numerous regulations (if they fall into the scope of “employees”), or they fall completely out of labour law’s sight. Yet, in real life, the distinctions between different workers are hardly binary. So, the need for a more nuanced regulatory apparatus becomes clear. This chapter, written for a book in honour of Arthurs, briefly assessed two concrete proposals he made for the adoption of an intermediate category between “employees” and independent contractors. I start by describing the original proposal in made in 1965, then move to explain the logic behind intermediate categories in this area, before assessing the Canadian legislated definition (which adopted Arthurs' proposal to some extent), explaining its deficiencies. I then move to analyze a more recent proposal made by Arthurs for the adoption of an intermediate category (along the same lines), suggesting some amendments that could further improve it.
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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 | 0.007 | 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 | 0.014 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".