Online Readers’ Comments as Popular Texts: Public Opinions of Paid Duty Policing in Canada
Bibliographic record
Abstract
Background Despite paid-duty policing and associated public opinions being an increasingly controversial topic in Canadian news media, there has been limited research about them.Analysis This article combines discourse and content analyses to examine the public opinions towards paid-duty policing in Canada expressed in the online readers’ comment sections of news articles. Conceptualizing comments as popular texts, the article discerns several themes, including police impartiality, reputation, expertise, and performance. Most comments centered on the economics of paid duty.Conclusion and implications The article concludes by considering why economics prevailed over other themes and reflects on core concepts in the literature on online comment boards, including interactivity and counter-publics.Keywords Public opinions; Readers’ comments; Discourse analysis; Content analysis; Audience reception; Media theory; PolicingContexte Il y a eu peu de recherches sur les services rémunérés de la police ou sur l’opinion publique envers ceux-ci, même s’ils deviennent un sujet de plus en plus controversé dans les médias canadiens.Analyse Nous effectuons des analyses de discours et de contenu pour examiner les sections de commentaires en ligne accompagnant certains articles sur l’actualité. En envisageant ces commentaires comme textes populaires, nous discernons divers thèmes relatifs à la police, y compris son impartialité, sa réputation, sa compétence et sa performance. La plupart des commentaires font mention de l’économie des services rémunérés.Conclusion et implications Nous terminons notre article en considérant pourquoi l’économie a prévalu sur d'autres thèmes dans les commentaires en ligne et en nous interrogeant sur des concepts centraux dans la recherche sur ce sujet tels que l’interactivité et les contre-publics.Mots clés Opinions publiques; Commentaires des lecteurs; Analyse du discours; Analyse de contenu; Réception publique; Théorie des médias; Maintien de l’ordre
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".