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Record W2784660874

Expanding Food Justice: Gender, Race and Hunger in Toronto

2017· dissertation· en· W2784660874 on OpenAlexaboutno aff
Stacey Murie

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsRace (biology)Economic JusticeGender studiesPolitical scienceSociologyGerontologyMedicineLaw
DOInot available

Abstract

fetched live from OpenAlex

Hunger is a significant issue in Toronto, particularly among racialized individuals and female lone-parent households. Canadian critical food scholarship generally attributes hunger among these marginalized groups to financial insecurity, disregarding the ways gender and race intersect with poverty. To challenge this pattern, I argue that a food justice framework can elucidate how intersecting systems of oppression impact the ability of marginalized communities to access food. Drawing from participant observation at The Stop, a leading food justice organization in Toronto, and interviews with staff members from various food security organizations, this project examines the utility of a food justice framework in understanding how gender and racial inequality shape the experiences of The Stopâ s participants and analyzes how The Stop works to dismantle gender and racial injustices through food activism, and what barriers to this work exist. Doing so highlights the possibilities for re-framing how hunger is understood and addressed in Toronto.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0220.012
Scholarly communication0.0050.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.254
Teacher spread0.233 · 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 designQualitative
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
Published2017
Admission routes1
Has abstractyes

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