Re-MEDIAting distant impacts - how Western media make sense of deforestation in different Brazilian biomes
Bibliographic record
Abstract
Brazil plays a central role in Western depictions of and narratives on tropical deforestation. In this contribution, we gather a large text corpus from Western media outlets with articles on deforestation in the Brazilian Amazon and Cerrado biomes. The sources include outlets from Europe, the US, Canada and Australia and span a time period from the late 1980s to 2020. Leveraging several text-mining approaches, such as topic modeling and automated narrative network analysis, we disentangle the way that Western media have tried to make sense of deforestation in the Amazon and the Cerrado biomes. We show that the former has received disproportionately more news coverage, specifically in times of international concern over the Brazilian government’s commitment to tackle deforestation. Further, Western media frequently report on the struggles of indigenous populations in the Amazon, often following an essentialist depiction of these communities, while in the case of the Cerrado, traditional populations are hardly mentioned at all. Our findings provide a methodologically innovative and empirically grounded case for the often raised concern over a relative invisibility of the Cerrado biome and its traditional populations, which may help explain observed disparities in governance interventions.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".