MétaCan
Menu
Back to cohort
Record W2559587458 · doi:10.5304/jafscd.2015.054.014

Beyond Inclusion: Toward an Anti-colonial Food Justice Praxis

2015· article· en· W2559587458 on OpenAlexaffabout
Lauren Kepkiewicz, Michael Chrobok, Madeline Whetung, Madelaine C. Cahuas, Jina Gill, Sam Walker, Sarah Wakefield

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOppressionPraxisComplicityEconomic JusticeSociologyColonialismSolidarityContext (archaeology)Inclusion (mineral)InjusticeEnvironmental ethicsCapitalismPolitical scienceGender studiesLawPolitics

Abstract

fetched live from OpenAlex

Activists and academics have increasingly drawn on the concept of "food justice" in recent years. While this trend is encouraging, we argue that a focus on "inclusion" by these actors may actually work to reproduce inequitable relationships. Food justice research and practice should thus move beyond inclusion to connect food system inequities to interlocking structures of oppression, such as capitalism, patriarchy, white supremacy, and colonialism. In Canada, placing food justice in the context of ongoing processes of colonialism—and recognizing that no justice can happen on stolen land—is particularly important. While we make these suggestions, we do not claim to have all the answers; we struggle through the same tensions we raise here in our own work. Nonetheless, we feel that encouraging those interested in food activism to consider intersecting systems of domination, to challenge such structures and their complicity in them, and to build solidarity with other activists, perhaps using land as the basis for new conversations and alliances, may be key steps toward cultivating an anti-colonial food justice praxis.

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)0.032
metaresearch head score (Gemma)0.022
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 score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0220.074
Scholarly communication0.0210.026
Open science0.0030.024
Research integrity0.0140.025
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.232
Teacher spread0.194 · 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

Citations37
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Agriculture Food Systems and Community DevelopmentSame topicOrganic Food and AgricultureFrench-language works237,207