The connection between wildlife co-management and indigenous well-being: What does the academic literature reveal?
Bibliographic record
Abstract
Globally, co-management systems have emerged as negotiated agreements designed to share responsibilities among Indigenous Peoples and State governments for the management of fish and wildlife. Co-management practitioners and policies regularly make decisions that influence the ways in which Indigenous Peoples interact with the lands, waters, and natural resources. The goal of this systematic critical review was to characterize the ways in which published research on co-management governance systems in Canada did or did not explicitly engage with Indigenous Peoples’ health and well-being as a key focus. ProQuest®, Web of ScienceTM, and JSTOR® databases were searched to identify literature published from 1973 to 2020 about co-management systems in Canada. The citations and articles were screened for relevance by two independent reviewers, using inclusion criteria developed a priori. Relevant articles were analyzed descriptively and qualitatively, using a health and well-being framework developed as an analytical framework for this study by integrating key attributes from three Indigenous social determinants of health models in Canada. The search resulted in 9,905 citations; 74 publications met the inclusion criteria and were analyzed. None of the included publications explicitly analyzed co-management from a public health or well-being lens; however, social determinants of health topics were implicit, prevalent, and connected to co-management throughout the literature. Social determinants of Indigenous Peoples’ health, such as land and ecosystems, food systems and security, Indigenous knowledge systems, culture, self-determination, and colonialism, were frequently represented in the co-management literature, even if not directly framed by the authors from a health perspective.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.032 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".