‘Facebook is the Devil’: Exploring Officer Perceptions of Cyber-based Harms Facing Youth in Rural and Remote Communities
Bibliographic record
Abstract
Policing research, still largely concentrated on urban contexts, is increasingly recognizing the unique features of police work in rural regions. Beyond notable differences such as lower overall levels crime and fewer (though more sporadically distributed) people, little is also known regarding rural police understandings and responses to online mediated harms, including relatively serious forms of cyberbullying, non-consensual ‘sexting’, and other forms of crime mediated online. Interviews with police officers (N = 42) here focus on their views regarding police work in response to cyber-mediated harm facing youth in rural and remote Atlantic Canada. Responses center on how rural regions play a role in mediating the nature of online conflict and police respond to such conflict. Officers highlight several related challenges, such as lack of parental support, and how some youth ‘define deviancy down’, referring to a lack of recognition regarding the harm caused by cyberbullying and non-consensual sexting (including issues related to the distribution of child pornography). Implications are discussed for research on rural policing where evidence-based practices remain lacking.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".