Migration decision-making and immigration policy: a qualitative case study of migration from Iraq to Canada
Bibliographic record
Abstract
Drawing on 18 in-depth, narrative interviews, this thesis responds to an emerging theoretical literature which seeks to understand how state immigration policy affects migrant decision-making with an empirical contribution. While economic and refugee migration are generally considered as separate phenomenon, this project samples research participants based on country of origin rather than entry status to Canada. It uses the case study of migration from Iraq to Canada following the 2003 US-invasion which provides an excellent opportunity to examine how immigration policies and migrants’ access to capital affect decision making, as those fleeing held high capital endowments and employed diverse mobility strategies to seek safety (Chaterland 2008; Chatty and Mansour 2011b). The thesis finds that immigration policy affects the composition of migrants throughout the migration process along class and gendered lines, and that treating research on economic and refugee migration as part of the same process allows for further understanding of decision-making than is possible when following the dichotomy. It also provides evidence to the suggestion by Fitzgerald and Arar (2018) that a New Economics of Labour Migration Framework, which treats the risk of violence as another risk to be managed by a household, is particularly useful to analyze migration decisions from conflict regions. However, these findings suggest that this framework should also include how legal frameworks affect decisions, how capital affects the options available to potential migrants, and how gender structures mobility and subsequent decisions to migrate
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.036 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".