Media and Climate Change Observatory Monthly Summary: No ‘silver lining’ - Issue 41, May 2020
Bibliographic record
Abstract
May 2020 has been a pivotal month in human history. Amid accelerated learning and intense behavior change, media attention to climate change and global warming at the global level increased slightly (0.2%) from April 2020 coverage. Compared to a year earlier (May 2019), much like the precipitous drop detected April 2019-April 2020, the number of news articles and segments about climate change and global warming in May 2020 dropped dramatically as well. Coverage May 1-31, 2020 through our Media and Climate Change Observatory (MeCCO) monitoring of 120 media sources in 56 countries and ten languages all across the planet has found a 52% drop from May 1-31, 2019. Regionally, the ongoing stream of stories in this past May increased in the Middle East (up 8%), Latin America (up 28%) and Oceania (up 30%). In contrast, coverage was decreased in Asia (down nearly 1%), Africa (down 22%) and North America (down 21%) from the previous month. Also in May 2020, coverage from international wire services – The Associated Press, Agence France Presse, The Canadian Press, and United Press International – dropped 4% from the month before while contracting 63% from May 2019.
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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) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.018 |
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; both teacher heads agree on what is shown here.
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".