Economic Immigration and Women: Not the Usual Story, Not the Usual Suspects
Bibliographic record
Abstract
The term ‘economic immigration’ can trigger multiple associations, from the high-rolling, risk-taking entrepreneur or the jet-setting IT specialist, to the vulnerable, ‘flexible’ migrant worker (Creese, Dyck and McLaren, 2008) hired in a plethora of low-paid, low-status occupations. However, when these terms are qualified further by adding ‘women’, the spectrum of images shrinks, as research on female labor migration in the global economy has ‘focused on a narrow range of sectors in, particularly, domestic work and sex work’ (Raghuram and Kofman, 2004, p. 95). Dominant, circumscribed representations of immigrant women not only fail to convey the richness of immigrant women’s economic migration experiences but also serve to undercut the scope of opportunities for women. Moreover, studies of how various im/migration priorities play out for women at subnational levels are only recently coming to the fore, and still mostly in select contexts (for Nova Scotia, see Dobrowolsky, 2011, 2012; Bryan, 2012; or for Toronto, see Buyan, 2012). Thus more comparative work on the interface between macro-forces and meso-scale immigration choices, calculations, and commitments at the provincial level in Canada, and those of immigrant women at the micro-scale, is required. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".