Political Ecology Across Spaces, Scales, and Social Groups
Bibliographic record
Abstract
Environmental issues have become increasingly prominent in local struggles, national debates, and international policies. In response, scholars are paying more attention to conventional politics and to more broadly defined relations of power and difference in the interactions between human groups and their biophysical environments. Such issues are at the heart of the relatively new interdisciplinary field of political ecology, forged at the intersection of political economy and cultural ecology. This volume provides a toolkit of vital concepts and a set of research models and analytic frameworks for researchers at all levels. The two opening chapters trace rich traditions of thought and practice that inform current approaches to political ecology. They point to the entangled relationship between humans, politics, economies, and environments at the dawn of the twenty-first century and address challenges that scholars face in navigating the blurring boundaries among relevant fields of enquiry. The twelve case studies that follow demonstrate ways that culture and politics serve to mediate human-environmental relationships in specific ecological and geographical contexts. Taken together, they describe uses of and conflicts over resources including land, water, soil, trees, biodiversity, money, knowledge, and information; they exemplify wide-ranging ecological settings including deserts, coasts, rainforests, high mountains, and modern cities; and they explore sites located around the world, from Canada to Tonga and cyberspace.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".