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Record W4253157222 · doi:10.32920/ryerson.14665614

"In Its Own Image" : Fashioning the Canadian Multicultural Mirage in Food Production, Consumption, and Equality

2021· preprint· en· W4253157222 on OpenAlexaffabout
Jenelle-Lara Gonzales

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsUniversity of GuelphToronto Metropolitan University
Fundersnot available
KeywordsCommodificationLamentImmigrationMulticulturalismPrivilege (computing)Consumption (sociology)ModernityCitizenshipSociologyDialecticCapital (architecture)Product (mathematics)Gender studiesPolitical scienceEconomyEconomicsLawSocial scienceArt

Abstract

fetched live from OpenAlex

With bucolic imaginings, it is commonplace to lament the social and physical distance that separates us from the production of our food. In a dystopic distanciation, food becomes a static product--commodified, fetishized, and objectified--while our relationship to it, increasingly antagonistic. Indeed, food provides a unique aperture into the 'malaises of modernity' (Taylor 1991) when 'simple' questions in fact reveal complex dynamics, processes, and symptoms covering a range of questions: From what is our food made? From where? And by whom? In highlighting the dialectic of the selective of producers, the unrestricted mobility of commodities and capital, and the immobility of land, this paper draws linkages between food, labour and migration through an analysis of their ordering principles that affront the 'privilege' of Canadian citizenship, the rights it confers, and the responsibilities it demands. For the study of immigration and settlement in Canada or more globally, Canada's active role in shaping the life conditions of the migrants it receives, these lines of inquiry cannot be ignored.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.525

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0260.026
Scholarly communication0.0100.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.052
GPT teacher head0.261
Teacher spread0.209 · 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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