Global environmental change: local perceptions, understandings, and explanations
Bibliographic record
Abstract
Global environmental change (GEC) is an increasingly discussed phenomenon in the scientific literature as evidence of its presence and impacts continues to grow. Yet, while the documentation of GEC is becoming more readily available, local perceptions of GEC- particularly in small-scale societies-and preferences about how to deal with it, are still largely overlooked. Local knowledge and perceptions of GEC are important in that agents make decisions (including on natural resource management) based on individual perceptions. We carried out a systematic literature review that aims to provide an exhaustive state-of-the-art of the degree to and manner in which the study of local perceptions of change are being addressed in GEC research. We reviewed 126 articles found in peer-reviewed journals (between 1998 and 2014) that address local perceptions of GEC. We used three particular lenses of analysis that are known to influence local perceptions, namely (i) cognition, (ii) culture and knowledge, and (iii) possibilities for adaptation.We present our findings on the geographical distribution of the current research, the most common changes reported, perceived drivers and impacts of change, and local explanations and evaluations of change and impacts. Overall, we found the studies to be geographically biased, lacking methodological reporting, mostly theory based with little primary data, and lacking of indepth analysis of the psychological and ontological influences in perception and implications for adaptation. We provide recommendations for future GEC research and propose the development of a "meta-language" around adaptation, perception, and mediation to encourage a greater appreciation and understanding of the diversity around these phenomena across multiple scales, and improved codesign and facilitation of locally relevant adaptation and mitigation strategies.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".