Do Labour Standards Improve Employment Relationships in Global Production Networks? A Cross-sector Study in Brazil
Bibliographic record
Abstract
Research on private regulation of labour standards in global production networks often highlights their continuing failure despite the fact that lead firms no longer consider them as mere window dressing. Fewer analyses delve into their on-the-ground effectiveness to benefit workers. This article joins a context-specific approach with quantitative analysis to examine whether labour standards used in private regulation improve employment relationships in suppliers of global production networks. Based on a single-country case study of Brazil, we look at the extent of their adoption by suppliers across sectors, their complementarity with national labour institutions, and whether the adoption of labour standards at supplier site level is likely to support labour agency. Our findings show little effectiveness of labour standards against those dimensions. The presence of labour standards at supplier level alone has no significant impact and varies greatly across sectors. It is only if workers are aware of the presence of such standards that it might support their agency when union membership is taken as proxy. Yet, the correlation could also be the other way round: awareness of labour standards depend on being a member of a union in the first place. KEYWORDS: private regulation; certification; labour standards; corporate social responsibility (CSR); global production networks
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".