Convivial Conservation Through Historical Reparations
Bibliographic record
Abstract
The dominant model of conservation, known as ‘fortress conservation’ for its focus on separating people and nature, has led to significant injustices and challenges. For over 30 years, political ecologists have documented how restrictive conservation practices marginalize the people who have historically safeguarded the very biodiversity that parks and biological reserves were created to protect. Yet this model remains dominant, especially through the 2002 Kunming-Montreal Global Biodiversity Framework ambition to put 30% of the planet under protected areas by 2030 (the so-called 30×30 target). Although participatory practices have become a key feature of current conservation policies, much more needs to be done for indigenous peoples and local communities to have meaningful decision-making powers in protecting their living environments. One key step is dealing seriously with historical conservation injustices through territorial and other reparations. As part of a broader attempt to develop and promote convivial conservation as an alternative global conservation paradigm, we build on Kyle Whyte&s;s idea of ‘relational tipping points’ to place historical reparations at the centre of conservation discourse and practice. In this way, we build on and expand political ecology scholarship on and commitment to activism and praxis that thoroughly embraces peoples’ rights to exercise autonomy in their own territories of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".