Labour Recruitment, Circuits of Capital and Gendered Mobility: Reconceptualizing the Indonesian Migration Industry
Bibliographic record
Abstract
During the last decade there has been a marked shift in the structure of migration from Indonesia with the deregulation of the transnational labour recruitment market after the fall of Suharto and a broader attempt across the region to regulate migrant flows to and from receiving countries in the wake of the Asian economic crisis. In this process, hundreds of Indonesian labour recruitment agencies have come to function as brokers in an increasingly government-regulated economy that sends documented migrants to countries such as Malaysia and Saudi Arabia. Based primarily on fieldwork on the island of Lombok, one of the major migrant-sending areas in Indonesia, the article considers the gendered aspects of this state market relationship by focusing ethnographic attention on the initial stages of recruitment, as informal labour brokers deliver migrants to formal agencies. Critically, the article describes how capital increasingly flows "down" towards female migrants and "up" from male migrants i.e., men must go into debt while women do not pay (or are even offered money) to travel abroad thus highlighting the gendered dimensions of the current economy of transnational migration. More generally, the article argues for a renewed focus on the migration industry as a way of reconceptualizing Indonesian transnational migration in the context of contemporary forms of globalization.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".