Reflections on the Future of Legal Segmentation: Three Frontiers of Investigation
Bibliographic record
Abstract
Abstract The examination of the legal constitution of work serves as a preamble to discussion of the three frontiers of segmentation that follows. Beyond labour market work examines the significance of work at the margins or beyond the labour market, highlighting how such work is affected by—and even incorporated into—law, policy and official practice and marking the extent to which it remains normatively and practically connected to work in the market economy. Beyond labour standards identifies the adjudication of labour standards as itself a potential site of segmentation. It then takes up the legal foundations of bargaining power at work in more detail, making the case for the adoption of a broader ‘law of work’ in the analysis of segmentation. Drawing on a model already employed to taxonomise family law, it illustrates a range of ways in which rules and norms might indirectly as well as directly govern work. Beyond labour and social institutions explores the effects of the governance projects and interventions of the international financial institutions on the segmentation of work and the distribution of benefits and losses among workers, drawing by way of illustration on the segmenting effects of the policy and regulatory management of recent economic and epidemiological crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.091 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".