No Longer a “Damsel in Distress”: Indonesian Migrant Returnee Women Living in a City
Bibliographic record
Abstract
Stories and images of Indonesian women working overseas as domestic and factory workers or in so-called low-skilled occupations are becoming increasingly familiar. The majority of the stories are distressing and heartbreaking, dominated by tragic accounts that continue to strengthen discursive constructions of migrant women’s vulnerability. In this paper I want to put a different spin to the current discourse of TKW in Indonesia. More specifically, I want to begin to talk about former TKW who have now returned to Indonesia after their employment overseas. When the identity of these women are extracted, and framed in a single dimension and when the memory of migrant workers is thus collective as opposed to individual, how can we truly consider femininity and gender of an Indonesian migrant woman? In order to build more dimensions to this story, I take a group of women returnees who are disrupting such workings I discussed earlier that push women into so-called margins, migrant worker returnee-turned activists who advocate on behalf of migrant women workers both at home and overseas. I argue these migrant returnee-turned activists display a different brand of collective consciousness that one might expect from TKW, and instead occupy a place of innovation and transformation in the city, and confound and subvert gender-specific conceptualization of migrant women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| 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".