Comment moderation strategies in Canadian news media and their principles
Bibliographic record
Abstract
This paper offers a critical examination of Canadian news guidelines and policieson user-generated content (UGC) posted on news-related comment sections andsocial media.The outline of how news-related UGC is moderated within Canadian news is achievedby looking at the online comment policies of major Canadian news organizations like .the CBC, CityNews (Halifax), CTV News, Glacier Media, Global News, The Globeand Mail, Narcity Media, Postmedia, QUB (Québecor), and Torstar.The policies highlight how Canadian news organizations have practical strategiesto manage news-related UGC but also call upon positive and negative socialprinciples, to flag ill practices, foster democracy, and fight against online hate speechand libel. The analysis shows how guidelines reflect many of the key principles highlightedin the literature review but make no reference to economic principles thatare emphasized as important in academic and journalist concerns with moderatingnews-related UGC.
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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 | 0.042 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".