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Record W2333080697 · doi:10.15353/cfs-rcea.v3i1.144

Constituting community through food charters: A rhetorical-genre analysis

2016· article· en· W2333080697 on OpenAlexaffvenueabout
Philippa Spoel, Colleen Derkatch

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2016
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsToronto Metropolitan UniversityLaurentian University
Fundersnot available
KeywordsRhetorical questionCharterAction (physics)SociologyIdeologyFood systemsFood securityIdentity (music)Listing (finance)Political scienceLawAestheticsHistoryPoliticsBusinessAgricultureArtLiterature

Abstract

fetched live from OpenAlex

Communities across Canada are increasingly developing food charters, with at least 22 regional charters published in Ontario alone. As a rhetorical genre, food charters are persuasive actions that articulate not only the kind of food system to which a community aspires, but also the kind of community that it aspires to be. We argue that Ontario’s food charters play an important role in constituting a sense of community identity and values through the rhetorical action of the genre itself. We analyze how this is accomplished through two rhetorical features, the naming of community and the listing of community priorities, showing how these features simultaneously obscure and reveal ideological tensions and logical incongruities within each community’s vision for its food system. Our analysis illustrates how the genre of the food charter both responds to and shapes the diverse, possibly conflicting values that inform food policy and food security initiatives in Ontario, and it offers insight into how the genre itself may inadvertently constrain the action it is intended to perform.

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)0.012
metaresearch head score (Gemma)0.024
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 score0.448
Threshold uncertainty score0.901

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0100.010
Science and technology studies0.0140.020
Scholarly communication0.0130.006
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.375
GPT teacher head0.422
Teacher spread0.047 · 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

Citations9
Published2016
Admission routes3
Has abstractyes

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Same venueCanadian Food Studies / La Revue canadienne des études sur l alimentationSame topicFood Security and Health in Diverse PopulationsFrench-language works237,207