A SCOPING REVIEW PROTOCOL ON THE SIGNIFICANCE OF SUSTAINABLE INDIGENOUS SEAFOOD SYSTEMS
Bibliographic record
Abstract
Indigenous populations mainly utilise natural resources to survive and those living along coastlines will depend on seafood for sustenance and income. Their diet and lifestyle are rooted in cultural spiritual and environmental connections. Today, modern development has posed sustainability risks to native communities due to environmental degradation, overfishing, and climate change. This scoping review protocol was developed and registered in the Open Science Framework (https://osf.io/sj4bn) and aims to assess the current state of Indigenous seafood systems, focusing on their strengths and challenges in advancing food sustainability. Based on the Population-Concept-Context (PCC) framework, the study will systematically compile articles from electronic databases like MEDLINE, EMBASE, and Web of Science, alongside grey literature, examining aspects such as food security, environmental impact, economic viability, and social dimensions. The review analyses seafood systems by geographical location, population, and production process, identifying factors that influence Indigenous livelihoods. Findings will highlight key aspects of Indigenous seafood systems and provide a balanced view of their positive and negative aspects. This review will offer insights valuable to policymakers, researchers, and practitioners to promote sustainable practices, addressing current gaps and opportunities within Indigenous food systems. Ultimately, the study aims to support sustainable management practices that strengthen the resilience, knowledge and well-being of Indigenous communities, besides promoting food security.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.139 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.078 | 0.017 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".