Use of online social media networking sites: An exploration of the impact on a major law enforcement agency
Bibliographic record
Abstract
The popularity of social media sites has grown exponentially over the past few years. However there is limited research regarding the impact that online social media networking sites have on a major law enforcement agency. This study investigates whether the use of online social media networking sites impact the actions and behaviors of police officers within the Edmonton Police Service (EPS). Six participants from the EPS were interviewed for this research project using a semi-structured, in-person interview methodology. Findings were analyzed under an exploratory approach in order to determine the connection between social media sites and the impacts on the individual participants interviewed. The results of this study find that online social media networking sites do have a direct impact on the actions and behaviours of police officers. The study identified three distinct themes; 1 – the police value social media as an investigative tool and communication channel, 2 – the general public’s use of social media sites are predominately used negatively toward the police, and 3 – some police officers intentionally alter their actions or behaviours due to the high probability of being captured on video. An awareness and understanding of the use of online social media networking sites, by the police and the general public, highlight the impacts on the behaviours and actions of police officers within the EPS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| 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".