Violent Victimization against Women in Canada: Evidence from the General Social Survey 2009 Data, a Gendered Study
Bibliographic record
Abstract
This study aimed to examine victimization against women in Canada. Statistics Canada General Social Survey (GSS) 2009 data set was used in this current study for the analysis. In all, 31,510 household were surveyed and out of that, 19,500 responses representing 61.6% were obtained for the GSS 2009, a sample which was smaller than the 24000 which was used for the 2004 general social survey. In this study, the short version of the GSS 2009 which has a sample of 1512 was used for the analysis. At the end of the study, it was revealed that there is no statistically significant difference regarding the experience of victimization in both males and females in Canada (p-value = 0.418, Lambda = 0.003, Phi = 0.21, Cramer’s V = 0.21. However, the results of the study revealed a significant difference the impacts of victimization of males and females respectively. Thus women are more likely to experience depression after being victimized than men (p-value = 0.000). Finally outcome of the study showed that respondents living in Urban neighborhoods were more likely to experience violent victimization than those in rural communities (Lambda = 0.000, Phi = 0.106, Cramer’s V = 0.106 and p-value = 0.000). The study therefore recommends that policies and programs to address violence against women need to be sustainable, properly financed, and parcipatory-involving not only women but men. Also comprehensive victim support systems are essential, ecompassing legal and counseling since the study indicated women experience more of the negative impacts after being victimized.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".