Decolonizing conservation, a global conversation: views from Turtle Island, Tanzania, and Thailand
Bibliographic record
Abstract
What does it mean to decolonize conservation? This question was posed to a group of scholars and activists working in different places around the world – the US, Canada, Tanzania, and Thailand. This article is an edited transcript of the conversation that ensued. The goal of this paper is to keep that conversation alive and continue to add nuance and curiosity to the question as it unfolds in similar and different places around the world. A key feature of continuing important dialogues such as this one, is to resist the temptation to offer definitive definitions of what it would take to decolonize conservation but rather seek out greater understanding of what it might look like in a variety of places. Amongst deepening calls for greater Indigenous inclusion as states seek to implement the Global Biodiversity Framework, it is vital we keep questions of what constitutes decolonized conservation top of mind.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".