Routledge Handbook of Climate Change Impacts on Indigenous Peoples and Local Communities
Bibliographic record
Abstract
This Handbook examines the diverse ways in which climate change impacts Indigenous Peoples and local communities and considers their response to these changes. While there is well-established evidence that the climate of the Earth is changing, the scarcity of instrumental data oftentimes challenges scientists’ ability to detect such impacts in remote and marginalized areas of the world or in areas with scarce data. Bridging this gap, this Handbook draws on field research among Indigenous Peoples and local communities distributed across different climatic zones and relying on different livelihood activities, to analyse their reports of and responses to climate change impacts. It includes contributions from a range of authors from different nationalities, disciplinary backgrounds, and positionalities, thus reflecting the diversity of approaches in the field. The Handbook is organised in two parts: Part I examines the diverse ways in which climate change – alone or in interaction with other drivers of environmental change – affects Indigenous Peoples and local communities; Part II examines how Indigenous Peoples and local communities are locally adapting their responses to these impacts. Overall, this book highlights Indigenous and local knowledge systems as an untapped resource which will be vital in deepening our understanding of the effects of climate change. The Routledge Handbook of Climate Change Impacts on Indigenous Peoples and Local Communities will be an essential reference text for students and scholars of climate change, anthropology, environmental studies, ethnobiology, and Indigenous studies. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.019 |
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".