Exploring adult cyber-harassment: key predictors of victimisation
Bibliographic record
Abstract
This exploratory study assesses the correlates of adult cyber-harassment victimisation. Using data from an original survey of Canadian adults aged 25 or older (N = 948), we present descriptive and multivariate analyses which demonstrate that cyber-harassment does extend into adulthood and has significant impacts that should not be trivialised as youthful deviance. Linear regression modelling indicates that higher rates of victimisation are predicted by age, gender identity, sexual orientation, disability status, financial insecurity, internet use behaviours, and privacy calculus, which together suggest that experiences of cyber-harassment may intersect with broader inequalities and experiences of marginalisation. Additional logistic regression modelling shows that gender, internet use behaviours, and fear of victimisation are factors associated with support seeking behaviours and reporting one’s victimisation to the police. Overall, our findings add to the larger existing literature on youth victims and suggest that adult cyber-harassment is an overlooked issue that requires more scholarly attention to better inform broader responsive policies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".