Trends and patterns in sustainability-related media coverage: A classification of issue-level attention
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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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".