Impossible or Inevitable?: Suggestions for Future Youth - Police Communication Online
Bibliographic record
Abstract
A lack of provincial standardization for social media use by Ontario police officers has limited the progression and success of community policing among young populations. The abundant success of police-youth communications in-person is evident in studies from studies by Anderson et al. (2007), Hinds (2007), and Leroux & McShane (2017); these results suggests that increased social media communications between youth and officers would prove beneficial. However, the barrier between the community policing principles outlined in Ontario’s Mobilization and Engagement Model (MEM) and actual police practice echo structural issues that have plagued Ontario policing for decades. Recent literature from Hawkes (2016) and earlier literature from Leighton (1991) demonstrate the ongoing struggle to translate theory into practice. Combining a qualitative content analysis of Twitter data alongside semi-structured interviews with police officers, this study identified MEM strategies used by officers on social media, as well as additional strategies introduced by officers on an individual basis. Findings indicate that there are inconsistencies between officer perceptions of their communications with youth and that of their actual practice. The discovery of four additional strategies used to accomplish community policing on social media suggests that the MEM should be restructured to accommodate for technological advances. Officer social media use varied but a strong commonality included the fear of damaged reputation or job loss-- indicating a greater need for standardization to instill confidence in officer social media use. While provincial standardization would benefit officers, it should not be restrictive as humanistic elements such as information dissemination and personalization derived from officer freedom on social media were most often noted as beneficial to both officers and youth.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".