Bibliographic record
Abstract
Shrimp aquaculture in Bangladesh linking the European Union, the USA, and Japan exhibits several characteristics of a buyer-driven global commodity chain (GCC). The study shows that, along with the effects of local conditions, buyers’ pressures transmitted through the GCC affect gender and employment relations in the lower segments of the chain. It has been found that the feminisation of the workforce in aquaculture is accompanied by the marginality of females, who receive lower wages and social prestige than their male counterparts, and who are mostly concentrated at the beginning and end of the local supply chain, with very limited access to other important nodes of the GCC. In addition to being flexible (part-time, temporary, casual), much of the employment in Bangladesh shrimp aquaculture is also informal, without an employment contract or its associated rights. As there is an apparent gap between labour standards in private regulatory regimes and actual labour practices in the production and processing segments of the chain, the pressing question concerns how the structure of the commodity chain can allow companies to maintain the flexibility and low labour costs required for international competitiveness while ensuring more equitable and empowering labour market outcomes for workers.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".