Reconstituting Thailand's Technology-intensive Shrimp Farms Through Gendered Migration
Bibliographic record
Abstract
"Thai shrimp farm owners'cost efficiency goals complement the needs for a conjugal home and workplace by migrant couple workers from Laos, Myanmar and northeast Thailand, which in turn has created a 'emigrant slot' among Surat Thani Province's shrimp farms. The conjugal workforce in shrimp farms is however differentiated by the creation of the female worker subject, publicly defined as 'not a real worker.' By paying migrants a couple wage, employers re-create and solidify discourses on the work and labor capacities that differentiate women and men in shrimp farms. Women workers for their part acquiesce to 'not being a real worker' in order to achieve certain ends, such as exploring supplementary income sources or creating latitude for childcare. Only Thai women, however, are able to find other income sources, whereas Burmese and Lao workers are largely tied to husbands and employers due to existing legal impediments. Women workers'enactments of 'not being a real worker' thus in turn reproduce and differentiate migrant national subjects engaged in the fisheries sector of Thailand. The paper argues that the production of gender and migrant differentiated identities constitute technology-intensive shrimp farming and its premium place in Thailand's export economy. By a focus on identities constituted by resource use, this paper puts in question essentialist and reified assumptions about gender and gender differences. Instead, we place social practices that produce gender subjects and their ontological differences at the center of analysis, thereby attentive to the diversity of subject positions available to different women in a single context."
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".