Media coverage of reports published by the Québec Ombudsman: an automated content analysis
Bibliographic record
Abstract
This article examines media coverage of reports published by the Québec Ombudsman, a body that upholds the rights of citizens and that goes by the name of ‘Public Protector’. A large part of the Québec Ombudsman’s mandate is to conduct investigations and issue recommendations following infringements by Québec’s administrative apparatus that affect one or several citizens. These infringements are reported to the ombudsman by citizens, which means that it must be visible to the public. Such visibility relies, to a great extent, on the media, hence the importance of analysing the Québec Ombudsman’s media coverage, a subject that has received little attention in the academic literature. Our article reveals that media coverage of the ombudsman’s reports is inconsistent. We also observe that, on average, newspaper articles adopt a more negative tone than the reports themselves. However, contrary to our expectations, reports with a more negative tone are not necessarily given more media coverage. The best predictor of the presence or absence of media coverage and tone congruence between reports and articles appears to be the presence of a press release issued by the ombudsman. Points for practitioners This article examines media coverage of reports published by the Québec Ombudsman. Based on an automated content analysis, it appears that the media coverage of the reports is not explained by the tone used in the documents published by the Québec Ombudsman. Reports that are more negative are not necessarily given greater coverage by journalists than positive reports. Direct communication efforts with the media (e.g. a press conference and the publication of a press release) are more likely to lead to media coverage.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.031 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".