Balance as bias, resolute on the retreat? Updates & analyses of newspaper coverage in the United States, United Kingdom, New Zealand, Australia and Canada over the past 15 years
Bibliographic record
Abstract
Abstract Through this research, we systematically updated and expanded understanding of how the print media represent evidence of human contributions to climate change. We built on previous research that examined how the journalistic norm of balanced reporting contributed to informationally biased print media coverage in the United States (U.S.) context. We conducted a content analysis of coverage across 4856 newspaper articles over 15 years (2005–2019) and expanded previous research beyond U.S. borders by analyzing 17 sources in five countries: the United Kingdom (U.K.), Australia, New Zealand, Canada, and the U.S. We found that across all the years of analysis, 90% of the sample accurately represented climate change. In addition, our data suggests that scientifically accurate coverage of climate change is improving over time. We also found that media coverage was significantly less accurate in 2010 and significantly more accurate in 2015, in comparison to the sample average. Additionally, Canada’s National Post, Australia’s Daily Telegraph and Sunday Telegraph, and the U.K.’s Daily Mail and Mail on Sunday (all historically conservative outlets) had significantly less accurate coverage of climate change over this time period than their counterparts.
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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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".