Confronting colonial history: toward healing, just, and equitable Indigenous conservation futures
Bibliographic record
Abstract
Against the backdrop of growing concerns for environmental and social justice, interest in developing effective strategies that support social and ecological resilience and recovery are mounting. To pursue these strategies requires cultivating a shared understanding of the full scope of settler colonial legacies that continue to impede justice efforts in conservation and environmentalism more broadly. However, although decolonial resources are growing, they remain scattered across various bodies of work and disciplines, often failing to be incorporated into conventional conservation understanding. Discounting these resources in mainstream science literature creates an immense challenge for conservation practitioners, scholars, and other professionals aiming to build their environmental justice and decolonial understanding. In alignment with these decolonial needs, we provide a brief primer of the origins of settler colonial conservation, resulting broadscale disparities, and pathways toward a more just conservation future. This synthesis of conservation’s colonial roots draws from diverse bodies of work, across disciplines and expert voices, and provides an entry point for cultivating a deeper understanding of justice and decolonization in conservation while centering the histories, realities, and futures of Indigenous Peoples worldwide.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.061 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| 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".