Plotting the coloniality of conservation
Bibliographic record
Abstract
Contemporary and market-based conservation policies, constructed as rational, neutral and apolitical, are being pursued around the world in the aim of staving off multiple, unfolding and overlapping environmental crises. However, the substantial body of research that examines the dominance of neoliberal environmental policies has paid relatively little attention to how colonial legacies interact with these contemporary and market-based conservation policies enacted in the Global South. It is only recently that critical scholars have begun to demonstrate how colonial legacies interact with market-based conservation policies in ways that increase their risk of failure, deepen on-the-ground inequalities and cement global injustices. In this article, we take further this emerging body of work by showing how contemporary,market-based conservation initiatives extend the temporalities and geographies of colonialism, undergird long-standing hegemonies and perpetuate exploitative power relations in the governing of nature-society relations, particularly in the Global South. Reflecting on ethnographic insights from six different field sites across countries of the Global South, we argue that decolonization is an important and necessary step in confronting some of the major weaknesses of contemporary conservation and the wider socio-ecological crisis itself. We conclude by briefly outlining what decolonizing conservation might entail.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".