Indian hi-tech immigrants in Canada: emerging gendered divisions of labour
Bibliographic record
Abstract
In this thesis, I draw on the particular experiences of Indian hi-tech immigrants arriving in a growing Canadian technological cluster, the Waterloo Region, located in south-western Ontario. This bilateral pattern of international labour migration between India and Canada reflects both nationsʼ efforts to enhance their economic competitiveness in a global knowledge economy: India as a global exporter and Canada as an importer of knowledge professionals. The stereotypical association of Indian nationals with technology work brings both restrictions and opportunities for Indian hi-tech immigrants navigating a racialised as well as gendered technology labour market in the Waterloo Region. My main aim is to reveal a microcosm of gendered negotiations involving individual economic migrants, their skilled spouses, their employers and the welfare state, particularly in the guise of officials regulating migration and access to childcare. The complex set of individual behaviours, ideologies, attitudes and practices all contribute to the emergence and maintenance of, as well as challenges to, particular gendered divisions of productive and reproductive work among these new entrants to Canada, as they lose the significant employment, social and familial networks and supports that typically are available in India. These Indian newcomer families view their responsibilities to their family to be as significant as their engagement in the Canadian labour market, as well as the advancement of their individual careers. In practice, however, familial responsibilities remain a more significant aspect of womenʼs lives, reproducing gendered divisions of both paid and unpaid work that mirror traditional gender roles and ideologies. The labour market participation of this particular group of Indian hi-tech immigrants, and especially professional immigrant mothers, is limited by the non-recognition of foreign credentials and cultural and/or racial discrimination but perhaps to an even greater extent by the lack of sufficient provisions for reproductive work under Canadaʼs liberal welfare state.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.044 | 0.013 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".