Indigenous Food Systems Changes and Resiliency: Protocol for a Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Indigenous food systems (IFS) consider the complex relationships and connections between land, animals, plants, water, and people. These food systems may differ between regions, Indigenous cultures, and history; however, given the similar colonial histories and policies influencing Indigenous groups in Canada, the United States, Australia, and Aotearoa (New Zealand), the IFS changes and responses in these regions may follow similar trends. Climate change and pollution continue to impact the environment in catastrophic ways, and this, in turn, impacts IFS. However, to date, there has been no review of the literature on IFS, how they are changing, and how communities are responding to these changes. OBJECTIVE In this scoping review, we will summarize primary research in Canada, the United States, Australia, and Aotearoa related to IFS addressing the following questions: (1) What changes are IFS experiencing in the context of climate change and pollution? (2) What actions have been taken in response to IFS changes? (3) What are the characteristics of IFS research in peer-reviewed academic literature? METHODS We will use the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews and the Joanna Briggs Institute reviewer’s manual to inform the review process. MEDLINE, SCOPUS, International Bibliography of the Social Sciences, Sociological Abstracts, and the Bibliography of Native North Americans are the databases included in this review search. All screening and extraction have been supported by Covidence software (Veritas Health Innovation) with 2 independent reviewers conducting the abstract and full-text screening. We will map concepts and themes related to the research questions to contribute to the understanding of IFS within the academic literature and provide a narrative review of the outcomes. RESULTS The electronic database searches for this review were conducted in May 2021. Screening and full-text review were initially completed in the winter of 2022. We are currently in the process of compiling results and aim to share findings in 2023. CONCLUSIONS This review will provide valuable insight into current IFS needs by summarizing the peer-reviewed literature on how IFS are changing because of climate change and pollution and how communities are responding to these changes. The results of this review will be shared with Indigenous communities, through academic publications, community conversations, and conference presentations. CLINICALTRIAL OSF Registries osf.io/xrj87; https://osf.io/xrj87 INTERNATIONAL REGISTERED REPORT RR1-10.2196/41627
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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 | 0.113 | 0.254 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.200 | 0.040 |
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".