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.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".