Bibliographic record
Abstract
Abstract This article examines the status of the right to a safe and healthy working environment (OHS) in European Union (EU) Free Trade Agreements (FTAs) by employing a comparative approach. It analyses OHS in EU FTAs using diachronic and synchronic methods and compares the EU’s stance with the positions of the United States and Canada. The article determines that transnational labour governance relating to OHS is increasingly being streamlined as the EU has started linking trade concessions to binding OHS obligations. The convergence of the substantive coverage of labour obligations in FTAs has to date remained understudied. The European Commission has stressed that the described shift is due to the recognition of OHS as the fifth fundamental labour right in 2022. However, this article argues that there are also other motivations. The EU already started negotiating binding OHS obligations following the negotiation of the FTA with Canada (2014) and after the adoption of the ‘Trade for All’ strategy (2015) under pressure from advocacy networks. Such networks—who have long considered OHS as the next frontier in transnational labour governance—found an ally in EU-based workers experiencing wage erosion against the backdrop of retreating multilateralism and increased trade in global value chains.
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".