The Food Equity and Environmental Data Sovereignty (FEEDS) Project: Protocol for a Quasi-Experimental Study Evaluating a Digital Platform for Climate Change Preparedness
Bibliographic record
Abstract
BACKGROUND: Despite having the tools at our disposal to enable an adequate food supply for all people, inequities in food acquisition, distribution, and most importantly, food sovereignty, worsen food insecurity. The detrimental impact of climate change on food systems and mental health is further exacerbated by a lack of food sovereignty. We urgently require innovative solutions to enable food sovereignty, minimize food insecurity, and address climate change-related mental distress (ie, solastalgia). Indigenous communities have a wealth of Traditional Knowledge for climate change adaptation and preparedness to strengthen food systems. Traditional Knowledge combined with Western methods can revolutionize ethical data collection, engagement, and knowledge mobilization. OBJECTIVE: The Food Equity and Environmental Data Sovereignty (FEEDS) Project takes a participatory action, citizen science approach for early detection and warning of climate change impacts on food sovereignty, food security, and solastalgia. The aim of this project is to develop and implement a sustainable digital platform that enables real-time decision-making to mitigate climate change-related impacts on food systems and mental well-being. METHODS: Citizen science enables citizens to actively contribute to all aspects of the research process. The FEEDS Project is being implemented in five phases: participatory project planning, digital climate change platform customization, community-led evaluation, digital platform and project refinement, and integrated knowledge translation. The project is governed by a Citizen Scientist Advisory Council comprising Elders, Traditional Knowledge Keepers, key community decision makers, youth, and FEEDS Project researchers. The Council governs all phases of the project, including coconceptualizing a climate change platform, which consists of a smartphone app and a digital decision-making dashboard. Apart from capturing environmental and health-related big data (eg, weather, permafrost degradation, fire hazards, and human movement), the custom-built app uses artificial intelligence to engage and enable citizens to report on environmental hazards, changes in biodiversity or wildlife, and related food and mental health issues in their communities. The app provides citizens with valuable information to mitigate health-related risks and relays big data in real time to a digital dashboard. RESULTS: This project is currently in phase 1, with the subarctic Métis jurisdiction of Île-à-la-Crosse, Saskatchewan, Canada. CONCLUSIONS: The FEEDS Project facilitates Indigenous Peoples' self-determination, governance, and data sovereignty. All citizen data are anonymous and encrypted, and communities have ownership, access, control, and possession of their data. The digital dashboard system provides decision makers with real-time data, thereby increasing the capacity to self-govern. The participatory action research approach, combined with digital citizen science, advances the cocreation of knowledge and multidisciplinary collaboration in the digital age. Given the urgency of climate change, leveraging technology provides communities with tools to respond to existing and emerging crises in a timely manner, as well as scientific evidence regarding the urgency of current health and environmental issues. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31389.
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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 | 0.069 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.091 | 0.019 |
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".