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Record W4285593288 · doi:10.15353/cfs-rcea.v9i2.531

Transformation or the next meal?

2022· article· en· W4285593288 on OpenAlexafffundvenue
Elizabeth Vibert, Bikrum Gill, Matt Murphy, Astrid V. Pérez Piñán, Claudia Puerta Silva

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousGovernment (linguistics)Political scienceWork (physics)CapitalismFood sovereigntyCapital (architecture)SovereigntyFocus groupEconomic growthGeographySociologyEconomyAgricultureFood securityAnthropologyLawEcology

Abstract

fetched live from OpenAlex

This article presents conversations across difference that took place among community partners and researchers at a week-long workshop in T’Sou-ke First Nation territory in 2019. The workshop launched the Four Stories About Food Sovereignty research network and project, which brings together food producers, activists, and researchers representing T’Sou-ke Nation in British Columbia, Wayuu Indigenous communities in Colombia, refugee communities in Jordan, and small-scale farmers in South Africa. We focus here on conversations that highlight global-local tensions in food justice work, the pressures of extractive economy, and pressures arising from climate crisis – challenges that some participants framed at the level of global extractivism and colonial-capitalism, others at the level of the soil. As the conversations reveal, there was more common ground than conflict in shared histories of dispossession, shared predicaments of extractive capital and its government allies, and shared concern to renew and reinvigorate ancestral practices of care for territory.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0170.020
Scholarly communication0.0120.016
Open science0.0010.009
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0350.006

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 head0.076
GPT teacher head0.226
Teacher spread0.150 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2022
Admission routes3
Has abstractyes

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