Community-based monitoring of Indigenous food security in a changing climate: global trends and future directions
Bibliographic record
Abstract
Abstract Climate change is expected to exacerbate existing food security challenges, especially in Indigenous communities worldwide. Community-based monitoring (CBM) is considered a promising strategy to improve monitoring of, and local adaptation to climatic and environmental change. Yet, it is unclear how this approach can be applied in food security or Indigenous contexts. The objectives of this paper are to: (1) review and synthesize the published literature on CBM of Indigenous food security; and, (2) identify gaps and trends in these monitoring efforts in the context of climate change. Using a systematic search and screening process, we identified 86 published articles. To be included, articles had to be published in a journal, describe a CBM system, describe any aspect of food security, and explicitly mention an Indigenous community. Relevant articles were thematically analyzed to characterize elements of CBM in the context of climate change. Results show that the number of articles published over time was steady and increased more than two-fold within the last five years. The reviewed articles reported on monitoring mainly in North America (37%) and South America (28%). In general, monitoring was either collaborative (51%) or externally-driven (37%), and focused primarily on tracking wildlife (29%), followed by natural resources (16%), environmental change (15%), fisheries (13%), climate change (9%), or some combination of these topics (18%). This review provides an evidence-base on the uses, characteristics, and opportunities of CBM, to guide future food security monitoring efforts in the context of climate change.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".