and To be published in the American Review of Canadian Studies
Bibliographic record
Abstract
This study assesses the fairness, accuracy, and comprehensiveness of U.S. newspaper coverage of the Canadian health system in two influential U.S. newspapers. Quantitative methods, interpretative assessments, and thematic analyses are employed to evaluate coverage of the Canadian health system between 2000 and 2005. Fifty articles from The New York Times and the Wall Street Journal met strict criteria for inclusion. Information from these articles is reviewed for accuracy and compared to published, peer-reviewed research. U.S. newspaper reporting on the topic of the Canadian health system is found to be poor. Points of misinformation are indicated, misrepresentations are specified, and inadequate explanations are denoted. The non-obvious, latent content of newspaper coverage is examined and omissions are identified. Explanations for these surprising results are considered. Overall, ongoing themes and controversial issues regarding the Canadian health system receive almost as much notice in U.S. newspapers as actual news events. Anecdotal information plays nearly as great a role in coverage as facts and evidence.
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.107 | 0.020 |
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".