The ABC Test: A New Model for Employment Status Determination?
Bibliographic record
Abstract
Abstract The tests for identifying who is an ‘employee’—the gateway for a multitude of employment rights—have preoccupied generations of labour lawyers. It is relatively rare, however, to see a significant change in the law itself in this area. We are currently witnessing such a rare change in the USA, where a new test called the ‘ABC test’ was adopted in California and is gaining support elsewhere. The new California test starts with a legal presumption of employee status. To rebut the presumption, the hiring party has to demonstrate that all the following conditions are satisfied: no control over the worker, the work is outside the usual course of the employer’s business and the worker is customarily engaged in an independently established business. The goal of this article is examine whether this new test is normatively better than previous tests and should be regarded as a model for legislation in other countries as well. Our assessment is made in light of three benchmarks: whether the new test successfully advances the purpose of labour laws, whether it adopts an optimal balance between selectivity and universalism, and whether it strikes an optimal balance between rules and standards. Our conclusion is generally positive, but at the same time we argue that some modifications are necessary to improve the test and make it a useful model.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".