Trends and patterns in sustainability-related media coverage: A classification of issue-level attention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sustainability has moved from fringe topic to headline news and key policy discourse in its own right. Yet, the sustainability discourse remains fragmented, with a diverse set of challenges receiving vastly different levels of attention. Nevertheless, the vast majority of previous studies have focused on media attention to climate change, whereas other sustainability challenges have received much less attention in the academic literature. In this paper, we explore trends and patterns in media coverage across a set of ten sustainability challenges. In particular, we are interested in the extent to which the recent trends and patterns in coverage that have been well-documented for climate change are reflected by other sustainability challenges. We utilise a sample of 23 broadsheet newspapers from five different countries (Australia, Canada, Germany, UK, US), covering a 17-year period from 2000 to 2016. Using the agenda-setting literature as a starting-point for our enquiry, we then turn to the toolset provided by financial econometrics to develop a basic typology of media attention focusing on the two dimensions information/noise and seasonality/non-seasonality. We find that media coverage on climate change, poverty and HIV/AIDS can mainly be characterized as information, whereas the remaining seven issues included in our study appear noise-driven. Seasonal patterns in coverage appear most pronounced for socioeconomic issues. Media attention to biodiversity and cleaner technologies has been crowded in by increased coverage on climate change. At the same time, we find clear divergences from overall trends and patterns at the level of different countries and individual newspapers.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it