Media and Climate Change Observatory Monthly Summary - June 2018
Bibliographic record
Abstract
June media attention to climate change and global warming was up 6% throughout the world from the previous month of May 2017. There were upticks in Asia (up 10%), the Middle East (up 34%), Africa (up 19%), Central/South America (up 38%), and Europe (up 12%), while holding relatively steady in Oceania. The Media and Climate Change Observatory (MeCCO) detected a decrease in coverage in North America (down 6%). However, the global numbers were down about 35% from counts a year ago (June 2017), when the high levels of coverage in June 2017 were largely attributed to reactions to United States (US) President Donald J. Trump’s withdrawal from the Paris Climate Agreement. At the country level, coverage held relatively steady from the previous month in Australia and New Zealand. Meanwhile, it went up from the previous month in India (+15%), Spain (+40%), the United Kingdom (UK) (+12%), Canada (+21%), and Germany (+56%), while it went down in the United States (-18%).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".