Catalysing Transformative Change in Conservation: Lessons Learned From a Decolonial Conservation Partnership
Bibliographic record
Abstract
Abstract Conservation faces a legitimacy crisis—inadequately protecting nature while marginalising the very people and societies shown to be most successful at conserving nature. Scholars and the global community are increasingly recognising that respecting, protecting, and elevating Indigenous land and water governance contributes to greater outcomes for people and nature. In the face of the dual climate and biodiversity crises, the challenge and the opportunity are great. True transformational change in the conservation sector will require sustained commitment, collaborative effort, humility, courage, and accountability within complex systems with many actors and interests. In this paper, and in the context of scholarly debates reconciliation and resurgence, we examine the efforts of the Conservation through Reconciliation Partnership (CRP)—an Indigenous-led, large-scale, multi-sectoral decolonial conservation partnership in Canada created to help elevate Indigenous-led conservation and support the transformation of the conservation sector. Drawing on our own experiences, public-facing materials the partnership has created, and the results of a mid-term evaluation, we assess the challenges and benefits of the CRP and offer key lessons for others wishing to create decolonial conservation partnerships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.033 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".