Social media strategies: Understanding the differences between North American police departments.
Bibliographic record
Abstract
Within a short timeframe, social media have become to be widely used in government organizations. Social media gurus assume that the transformational capacities of social media result in similar communication strategies in different organizations. According to them, government is transforming into a user-generated state. This paper investigates this claim empirically by testing the claim of convergence in social media practices in three North-American police departments (Boston, Washington DC and Toronto). The research shows that the social media strategies are widely different: the Boston Police Department has developed a ‘push strategy’ while the Metropolitan Police Department in DC has developed a ‘push and pull strategy and the Toronto Police Service a ‘networking strategy’. The paper concludes that a combination of contextual and path-dependency factors accounts for differences in the emerging social media strategies of government organizations. Social media have a logic of their own but this logic only manifests itself if it lands on fertile soil in a government bureaucracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".