Bibliographic record
Abstract
The need for publicity, transparency and accountability has increased as a pressure on police forces since the emergence of social media. But while initially the novelty and lack of experience with new tools excused police forces from uncertainties, shortcomings or mistakes in managing their profiles, a quarter of a century later, the police can be accountable for awareness and usefulness of their practice. The main question of the research, based on social media monitoring, is how the different branches of policing, their specific activities and the content that highlights expertise and promotes and recognises policing to citizens are presented, beyond the organisational image-building function. The applied method is the qualitative document and text analysis supported by computer software to analyse the Facebook activity of the Hungarian Police and a Hungarian (community) police officer in Canada. The sample included two different months’ shared content for both profiles studied using purposive sampling. The results show a strong contrast in terms of the direct law enforcement implications of the content. Hungarian communication is centralised, highly controlled, organisational and professional, but at a great distance from the day-to-day tasks, while Canadian communication is personal, semi-professional-layman and gives a direct insight into the working day of the police. Our conclusion is that different policing models enable very different content along different practices, and the reasons are rooted in both regulation and culture. While the Hungarian legislation relegates the individual police officer to the background in order to strengthen the image of the organisation through propaganda, overseas it is the individual who brings the profession ‘in the flesh’ and gives it credibility and legitimacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".