Media and Climate Change Observatory Monthly Summary: Scientists fear Arctic heating could trigger a climate ‘tipping point’ - Issue 27, March 2019
Bibliographic record
Abstract
March 2019 coverage was up 25% from February 2019, and nearly doubled from the amount of media attention to climate change or global warming in March 2018. March 2019 coverage was up 5% in Africa, up 9% in Oceania, up 19% in Asia, up 27% in the Middle East, and up 48% in Central/South America compared to the previous month. Among monitoring at the country level, coverage increased 4% in Australia, 10% in Germany, 12% in the United Kingdom (UK), 17% in Canada, 20% in India, 21% in New Zealand and 25% in the United States (US). Among the four wire services we now monitor, there was a 55% increase in March 2019 coverage of climate change or global warming from February 2019 coverage, and nearly a doubling of coverage from March 2018 levels.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.014 |
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".