Gendered Nature of Cyber Victimization as a Mechanism of Social Control
Bibliographic record
Abstract
This research used a deductive post-hoc statistical design and Statistics Canada’s 2009 General Social Survey on victimization to explore the social control function of cyber victimization and determine whether this is gendered. Social control was operationalized as a composite measure of self-responsibilization. A multiple regression analysis identified predictors of social control and additional multiple regression models were used for a gender specific examination of social control. A total of 14 predictor variables were entered into three blocks: cyber victimization; sociodemographic characteristics; and violent victimization in physical space. The results reveal that cyber victimization remains a significant predictor of social control in addition to gender, a number of other sociodemographic characteristics of respondents, and physical space victimization types. The findings suggest that the theory of social control, which has been applied to violence against women in physical space, can also be applied to cyber space victimizations. This study also provides insights into the compound effects of physical space and cyber space victimizations on women and identifies implications for policy, methods, and theories for addressing and examining violence against women in cyberspace.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".