Environmental influences on skilled worker migration from Bangladesh to Canada
Bibliographic record
Abstract
We conducted focus groups in Toronto with 44 recent skilled worker immigrants from Bangladesh to explore whether their decisions to migrate to Canada may have been influenced by environmental problems. Previous research has documented how floods, cyclones, droughts, and seasonal precipitation variations affect rural‐urban migration patterns within Bangladesh, and to its neighbours. Most participants had not experienced such environmental hazards, having lived in Dhaka prior to migrating. However, Dhaka's ongoing problems with air and water pollution, sanitation, lack of green space, and food adulteration were cited by 70% as being relevant considerations for the decision to migrate. The degree of influence varied considerably among participants. Roughly 16% said pollution was their primary motivation for leaving, household members having suffered from illnesses traceable to air pollution or poor sanitation. Another 54% stated that Dhaka's environmental problems were part of a wider range of quality‐of‐life concerns that had some influence on their decision. The findings suggest that current migration to Canada is not connected with environmental migration that takes place within Bangladesh, but that urban environmental problems combined with other social, economic, and political factors can help drive migration.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".