Exploring the Contribution of Environmental Non-Governmental Organizations to Indigenous-led Conservation in Canada
Bibliographic record
Abstract
In recent years several Canadian Environmental Non-Governmental Organizations (ENGOs) have publicly declared their commitment to conservation partnerships with Indigenous Nations and communities. However, a comprehensive understanding of the challenges and opportunities they experience is lacking. The current research project investigated how these partnerships contribute to advancing conservation projects, including Indigenous Protected and Conserved Areas (IPCAs). A review of literature underscored the importance of such partnerships, however the mechanisms to establish and maintain these relationships have received limited attention. To address this gap, a pilot survey of 5 national ENGOs was conducted, followed by semi structured interviews of representatives from 24 ENGOs and one Indigenous educational non-profit organization focused on conservation. Using the framework developed by Stein, Ahenakew, and Kui (2023), ENGO efforts to transform and decolonize conventional conservation approaches were categorized into four non-linear, non exclusive stages: representation, recognition, redistribution, and reparation. The responses most often fell within the “recognition” category, while the “representation” and “redistribution” categories were less frequently addressed. Notably, no responses fit well within the “reparation” category, highlighting a need for a more fundamental shift in how conservation is practiced. While ENGOs cannot achieve this transformation alone, the findings of this study indicate that ENGOs occupy a unique position in the sector which they should leverage to challenge colonial approaches and drive positive change.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".