Media and Climate Change Observatory Monthly Summary - Issue 17, May 2018
Bibliographic record
Abstract
Newspaper coverage in the Middle East and Oceania went down 35% and 23% respectively, while Asia dipped 10%, Europe diminished 6%, North America decreased 12% and African coverage was 18% lower than in April. Central/ South America dropped 8%, while coverage in Africa held relatively steady. At the country level in May 2018, newspaper coverage went down compared to April in Australia (-13%), New Zealand (-37%), India (-7%), the United Kingdom (UK) (-8%), Germany (-11%), and the United States (-17%). It held steady in Canada. Meanwhile, US television coverage decreased 14% from the previous month, while the six world radio sources monitored diminished 37% from coverage in the previous month. Global newspaper coverage was about 10% lower than counts a year ago (May 2017), when a great deal of global media attention was focused on the Trump Administration’s impending (June 1, 2017) decision on whether or not to withdraw from the Paris Climate Agreement. This primarily political story pervaded cultural, economic and societal stories in May 2017 as well. For example, journalist Alexandra Zavis from the Los Angeles Times covered President Trump’s visit with Pope Francis. During that visit on May 23, 2017, Zavis wrote about how the Pope provided the President with a copy of his 2015 encyclical that called for global collective action to address climate change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.352 | 0.226 |
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".