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Reflecting On The Past to Envision The Future: Visions for food security and food sovereignty in Northern Ontario First Nations

2025· article· en· W4408806936 on OpenAlexaffvenueabout
Justina Walker-Mohamed, Charlotte Potter, Paul Benalcazar, Dakota Cherry, Paul Sitsofe, Margarita Fontecha, Silvia Sarapura–Escobar, Firoz Alam, Shy-Anne Barlett, Michelle Seanor

Bibliographic record

VenueRural Review Ontario Rural Planning Development and Policy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVisionFood securitySovereigntyFood sovereigntyPolitical scienceFood insecurityInternational tradeEnvironmental planningDevelopment economicsGeographyBusinessPoliticsSociologyEconomicsLawArchaeologyAgriculture

Abstract

fetched live from OpenAlex

In 2024, Northern Ontario First Nations community members came together to share memories of past and present food systems, and to envision the future of food systems. Discussions from participatory focus groups and “wisdom dialogues” emphasized desires to be self-sustaining, to bring back traditions, and to (re)learn to use what already exists from the land. In this presentation, we will outline our approach to community-led research dialogues that foster processes of communication, knowledge exchange, and problem-solving in a non-hierarchical space that supports engagement, mutual respect, community research ownership and collaboration. We will highlight how we (research team, community partners) co-developed and implemented locally adapted methods and tools to understand and reflect on community memories of food systems and climate change - and how we moved towards co-analyzing the current food system challenges, and envisioning futures through an iterative, action-learning process. We will share some of the high level findings and outputs from research, and outline avenues for further research and action.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.387
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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