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Record W3214065657 · doi:10.32920/ryerson.14649102.v1

Widening the Circle: Racialized Immigrants in Toronto's Alternative Food Movement

2021· preprint· en· W3214065657 on OpenAlexaffabout
Hilda Nouri-Sabzikar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of GuelphToronto Metropolitan University
Fundersnot available
KeywordsImmigrationVisionMovement (music)Food securitySociologyFood studiesSocial justicePolitical scienceRace (biology)Gender studiesGeographyPolitical economyLawAesthetics

Abstract

fetched live from OpenAlex

Toronto is a growing site for the alternative food movement with plenty of innovative projects. While the alternative food movement may emphasize the participation of diverse members and communities some observers have noticed the underrepresentation of immigrants and visible minorities within the movement. As Toronto increasingly acts as an immigration hub, it becomes critical to create room for diverse and marginalized voices in food spaces. This major research paper will reflect findings from interviews with five food leaders in Toronto involved in food justice and food security initiatives while using critical whiteness theory and critical race theory to deconstruct the complexities which surround the needs and visions of immigrants and visible minorities. Findings reveal that when the voices of immigrants and visible minorities are recognized in the food movement, there is work to be done in improving accessibility, inclusivity and collaboration of the movement.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.284
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0420.014
Scholarly communication0.0060.002
Open science0.0010.008
Research integrity0.0020.003
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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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
Published2021
Admission routes2
Has abstractyes

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