Multi-Dimensional News Diversity During Social Unrest: U.S. vs. Canadian Coverage of COVID-19 Protests
Bibliographic record
Abstract
Background: It is crucial for democracies to ensure diversity in news content so as to offer varied journalistic perspectives. Yet how to define and measure this diversity is open to debate. Too often, the focus is on a single dimension. Analysis: Relying on a computer-assisted content analysis and a multivariate analysis, our study uses a multidimensional approach to examine 12,907 news articles on pandemic-induced protests in Canada and the United States from 2020 to 2022. Conclusions and implications: We found a more balanced coverage of topics in Canadian outlets, and a broader emotional range in U.S. articles. In general, corporate media tend to show polarized sentiments, a notable difference between independent media and media conglomerates. Our findings emphasize the need for a comprehensive approach to diversity in news content and challenge simplistic country-based categorizations of media systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".