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Record W4324028823 · doi:10.15353/cfs-rcea.v10i1.563

Deconstructing ‘Canadian Cuisine’: Towards decolonial food futurities on Turtle Island

2023· article· en· W4324028823 on OpenAlexaffvenueabout
Hana Mustapha, Sharai Masanganise

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSolidarityColonialismSociologyIndigenousFood sovereigntyPoliticsRacismFood systemsOppressionComplicityEnvironmental ethicsGender studiesFood securityPolitical scienceGeographyEcologyLaw

Abstract

fetched live from OpenAlex

As scholars and community activists, to secure a just food system, we must first acknowledge our complicity in hierarchal power structures that shape structural inequities by questioning the underlying socio-political currents and interrogating the dominant relationships within our food system. In this commentary, the authors reflect upon their intersectional lived experiences interacting with food systems in the settler nation of Canada. They explore the complex interplay of systemic racism, settler colonialism and neoliberalism within the Canadian food system by deconstructing the indefinable essence of “Canadian cuisine” and mapping these situated insights onto the process of gastronomic multiculturalism. The authors provide their perspective that an entry point along the ongoing process of securing decolonial food futurities on Turtle Island requires a conscious commitment to building interrelational solidarity across differences, reckoning with colonial land politics and supporting food sovereignty for both racialized communities and Indigenous Peoples.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.429

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 studies0.0330.036
Scholarly communication0.0070.003
Open science0.0020.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.239
Teacher spread0.187 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Food Studies / La Revue canadienne des études sur l alimentationSame topicCulinary Culture and TourismFrench-language works237,207