Is Hunting Still Healthy? Understanding the Interrelationships between Indigenous Participation in Land-Based Practices and Human-Environmental Health
Bibliographic record
Abstract
Indigenous participation in land-based practices such as hunting, fishing, ceremony, and land care has a long history. In recent years, researchers and policy makers have advocated the benefits of these practices for both Indigenous people and the places they live. However, there have also been documented risks associated with participation in these activities. Environmental change brought about by shifts in land use, climate changes, and the accumulation of contaminants in the food chain sit alongside equally rapid shifts in social, economic and cultural circumstances, preferences and practices. To date, the literature has not offered a wide-ranging review of the available cross-disciplinary or cross-ecozone evidence for these intersecting benefits and risks, for both human and environmental health and wellbeing. By utilising hunting as a case study, this paper seeks to fill part of that gap through a transdisciplinary meta-analysis of the international literature exploring the ways in which Indigenous participation in land-based practices and human-environmental health have been studied, where the current gaps are, and how these findings could be used to inform research and policy. The result is an intriguing summary of disparate research that highlights the patchwork of contradictory understandings, and uneven regional emphasis, that have been documented. A new model was subsequently developed that facilitates a more in-depth consideration of these complex issues within local-global scale considerations. These findings challenge the bounded disciplinary and geographic spaces in which much of this work has occurred to date, and opens a dialogue to consider the importance of approaching these issues holistically.
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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".