Bibliographic record
Abstract
In 1968, two years before the first Earth Day, the Apollo 8 mission captured Earthrise, the first color image of earth. Four years later, the Apollo 17 mission took the now iconic image, The Blue Marble, the first to encompass the whole Earth. These photographs have become the most recognizable images of our planet, broadcast across the globe through our media, marking the beginnings of a shift in our conceptualization of the planet. In this chapter, Callison charts the relationship between the media and the environment, highlighting the roles played by different media (television, radio, newspapers, etc.) in the development of new environmental policies, and exploring the different ways in which climate change, and the Earth, has been represented – for example, in its 1989 issue, Time magazine replaced its ‘Person of the Year’ with our ‘Planet of the Year’, beneath the headline, ‘Endangered Earth’. However, in the 1990s, media outlets began to shift their coverage away from environmental issues and science topics more generally, with some national media outlets questioning the legitimacy of climate change, despite progressively more urgent scientific reports being published. In this chapter, Callison examines media coverage of climate change through the last fifty years, addressing, among other themes, the slippery character of climate change as a news story, and the failure of media coverage to represent diversity in human experience and relations. In 2020, our myriad of handheld devices connect us globally in ways that weren’t imaginable fifty years ago; yet, despite this digital infrastructure, how connected we feel to each other, and how informed we are, is still equivocal.
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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.416 | 0.257 |
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".