Males Experiencing Sexual Assault at a Young Age: A Social Policy Evaluation in Canada
Bibliographic record
Abstract
The paper aims to examine whether there should be policy intervention in Canada specifically helping males experiencing sexual assault because the current literature about the causes and consequences of male sexual assault victimization was limited in Canada, and sexual assault policies in Canada were mainly for female victims. The secondary study was conducted using the 2014 General Social Survey, Cycle 28: Victimization main file, a dataset created by Statistics Canada. Two binary logistics regressions are conducted to examine the relationship between the sex of the respondent and the relationship between sexual assault victims and attackers, and the male's disability status and the likelihood of being sexually assaulted before age 15. Ordinary least squares regression is used to examine the impact of sexual assault victimization on one's mental health. The results based on the weighted analyses oppose the potential indicators identified by current literature; there is no statistically significant relationship with a 95% confidence level found in all three hypotheses after controlling the potential key indicators. Although the result suggests no policy interventions should target males, males, or males with physical or mental/psychological disabilities, the results may be more accurate by using a more updated dataset with more relevant questions and sociodemographic information available and more advanced statistical models and knowledge about quantitative research. The paper still suggests helping male sexual assault victims; other studies suggest that some face difficulties while seeking help. Anti-sexual assault policies should be created through prevention, reduction, legal responses, and various institutions and levels of authorities cooperating to solve the complex issue.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".