Epistemologies of bordering: Domestic violence advocacy with marriage migrants in the shadow of deportation
Bibliographic record
Abstract
Abstract Drawing on interviews with service providers and legal advocates in Canada, this article explores how bordering practices shape front-line service delivery with immigrant women seeking safety from domestic violence. Our research examines the implementation of ‘conditional permanent residence’ (conditional PR) between 2012 and 2017. Conditional PR applied to some newly sponsored spouses and partners who were required to cohabit with their sponsoring spouse/partner for two years following their arrival in Canada in order to retain their permanent resident status. We illustrate how conditional PR exacerbated the vulnerabilities already facing spousal immigrants by linking deportation to the failure to cohabit with their spouse. In particular, we examine the implementation of an ‘exception for abuse and neglect’, whereby victims of domestic violence could apply to remove the condition on their permanent resident status. We argue that when service providers mobilized their ‘ways of knowing’ about domestic violence to verify a sponsored spouse’s claims of abuse, they inadvertently took part in regulating ‘deserving’ versus deportable immigrants. This research develops a gendered analysis of deportability towards theorizing how bordering practices operate through the shadow state to regulate racialized immigrant women.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.042 | 0.080 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".