Gender, Dowry and the Migration System l 357 Gender, Dowry and the Migration System of Indian Information Technology Professionals
Bibliographic record
Abstract
The current literature on gender and migration focuses largely on womens experi-ences as migrants or, alternatively, on their experiences as those left behind. This article, on the contrary, seeks to demonstrate how gender is central in producing a migration system itself. Based on in-depth fieldwork in Sydney (Australia) and Andhra Pradesh (India) on the migration system of Indian information technology (IT) professionals from 2000 to 2001, the article suggests that the gender relations prevalent in Andhra Pradesh, particularly the institution of dowry, have been critical in producing a specially cheap and flexible labour force, and in supporting it in the volatile global economy. In turn, the emergence of a group of mobile IT professionals contributes to the increase of dowry, with disturbing consequences for those underprivileged and seemingly unconcerned with the IT industry. The spectacular growth of the information technology (IT) indus-try and the hyper-mobility of IT professionals are among the most significant social developments in India since its economic liber-alisation in the beginning of the 1990s. In 2002, 64,980 Indians were granted H-1B visas, the special work permit of the United States for highly skilled temporary migrants, far exceeding the second and third largest groups (China with 18,841 and Canada with 11,760) (US Citizenship and Immigration Services 2003: 153). Even more strikingly, 73 per cent of the Indian H-1B visa holders were computer professionals and 63 per cent of all the computer-related H-1B visas
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".