Immigrants’ Length of Residence and Stalking Victimization in Canada: A Gendered Analysis
Bibliographic record
Abstract
Although previous studies have explored the role of gender on stalking victimization, we know very little about how female and male immigrants are exposed to stalking victimization over time after their arrival to their host society. To address this void in the literature, we use the 2014 Canada General Social Survey to compare stalking victimization among native-born individuals, recent immigrants (those who have been in Canada for fewer than 10 years), and established immigrants (those who have been in Canada for 10 years or more) separately for women and men. Applying gender-specific complementary log-log models, we find that female (OR = 0.63, p < 0.05) and male (OR = 0.46, p < 0.01) recent immigrants are less likely to experience stalking victimization than their native-born counterparts. We also find that female established immigrants (OR = 0.65, p < 0.05) are less likely to experience stalking victimization than their native-born counterparts although there is no significance difference for male established immigrants (OR = 1.01, p > 0.05). Overall, this study points to the importance of understanding the intersection between immigrants’ length of residence and gender in the context of stalking victimization in Canada. Based on these findings, we discuss several implications for policymakers and directions for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".