The Fragmentation and Complexity of Labour Law, Effective Protections, and Better Work: An Analytical Framework
Bibliographic record
Abstract
Labour law is a fragmented regulatory landscape bringing together multiple legal sources drawn from various areas of law, institutions, and interactions between multiple regulatory tiers and frameworks. This article offers a conceptual and analytical framework for understanding the intricate contemporary structure of labour law and its effectiveness in ensuring the protections it has historically been intended to provide. The article considers four types of legal fragmentation — temporal, vertical, horizontal, and institutional — distinguished by character and effect. The fragmented architecture of legal regulation leads, unsurprisingly, to complexity. The article puts forward an analytical approach that assesses how labour law’s complexity and fragmentation impacts its effectiveness. The article first explains the process of mapping a fragmented regulatory landscape and outlines the analytical benchmarks for assessing legal effectiveness. The concluding section examines labour law as a fragmented landscape and the resulting normative dynamics.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".