An Intersectional and Ecological Approach to Undestanding Cyber-sexual Violence Vulnerability: Connecting Individual, Environmental, and Sociocultural Factors
Bibliographic record
Abstract
Cyber-sexual violence is an emerging social and public health concern, yet our knowledge and understanding of this issue is limited. In light of this, the purpose of the present study was to investigate the factors that create a vulnerability for cyber-sexual violence at each level of social ecology, using an ecological and intersectional framework as a guide. In order to fulfill the above research objective, a cross-sectional research design was employed in which a sample of Canadian adults completed an online self-guided survey. The results of this study indicate that cyber-sexual violence vulnerability can be conceptualized as emerging from an interplay among the individual level variables of age and gender identity; the microsystem level variables of time spent online, time spent using online dating sites, and engagement in sexting; as well as the chronosystem variable of sexual violence victimization history. Moreover, when considered independently, engagement in sexting and sexual violence victimization history were the sole variables that significantly predicted cyber-sexual violence vulnerability. The results of this study indicate that in order to establish effective cyber-sexual violence prevention and intervention, we must take into account the various aspects of an individual’s identity, environment, and experiences as well as the sociocultural context in which they exist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| 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".