A Strategic Approach to Regulating Unacceptable Forms of Work
Bibliographic record
Abstract
Upgrading low‐waged and insecure work is central to contemporary labour and development initiatives, from the UN Sustainable Development Goals to the United Kingdom ‘Taylor Review’. The International Labour Organization's notion of unacceptable forms of work (UFW) is a crucial contribution. Yet the regulatory frameworks that can effectively address UFW are unclear. This article builds on a novel framework ‐ the Multidimensional Model of UFW. Drawing on theoretical literatures at the frontline of regulation scholarship, it proposes a strategic approach to UFW regulation that supports development, acknowledges the constrained resources of low‐income countries, and targets expansive and sustainable effects. Two key concepts are identified: points of leverage and institutional dynamism. Globally‐prominent regulatory frameworks are assessed as a starting point for mapping the strategic approach: the Mathadi Act of Maharashtra, India; Uruguayan domestic work legislation; minimum wages in the global North and South; and United Kingdom regulation of ‘zero‐hours contracts’.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".