Local people's accounts of climate change: to what extent are they influenced by the media?
Bibliographic record
Abstract
Abstract Researchers using local and indigenous people's accounts of climate change in their scientific work often face scepticism regarding the value of such information. The critics' argument is that since local and indigenous people are often exposed to the global discourse on climate change, their observations and information may in fact be reproductions of science popularized through communication media. There are instances in which local people's accounts of climate change and impacts thereof may be influenced by how media frame and popularize scientific models and predictions. However, we propose several reasons why the influence of media reports and coverage of climate change is usually superficial. First, there are significant differences between the epistemologies employed by media and those of local people. Although media may be borrowing local environmental categories, they may be filling them with different content, leading to incoherence. Second, media accounts are often general and locally irrelevant, in contrast with the detailed local anchoring of the knowledge often held by people who rely on natural resources for their livelihoods. Their observations often rely on holistic ways of knowing their environments, integrating large numbers of variables, and the relationships between these. We propose that accounts based on such observations are probably not influenced by media framings and that uncovering their underlying ‘ways of knowing’ would provide valuable additional evidence in interdisciplinary studies of climate change. WIREs Clim Change 2013, 4:1–8. doi: 10.1002/wcc.199 This article is categorized under: Social Status of Climate Change Knowledge > Sociology/Anthropology of Climate Knowledge
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".