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Record W4254510407 · doi:10.32920/ryerson.14647824.v1

Eating identity: challenging narratives of Canadianness through culinary identity building

2021· preprint· en· W4254510407 on OpenAlexaffabout
Natalie Ramtahal

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsWomen's and Gender Studies et Recherches FéministesProfessional Engineers Ontario
Fundersnot available
KeywordsIdentity (music)NarrativePoliticsExpansiveIndigenousImmigrationDiversity (politics)PopulationGender studiesSociologyPolitical scienceGeographyAestheticsAnthropologyLawArt

Abstract

fetched live from OpenAlex

This MRP will examine how food can be used as a tool for challenging traditional nation stories and growing notions of what it means to be Canadian. There is an opportunity to write and shape a Canadian narrative that is inclusive of its evolving demography while simultaneously reconciling its violent history by developing a Canadian culinary identity. Food is political, social and cultural. Food can bring people together and can provide a platform to have compelling discussions about what it means to be Canadian; who is included in the definition of Canadian; and, how we can develop a sense of Canadianness that speaks to an evolving population. Historically and at present, Canada’s story has often excluded or minimized the cultural, political and social contributions of Indigenous peoples and racialized immigrants. There remains a prevailing sense of Canadian identity being tethered to whiteness despite over a century of global immigration. However, the very idea of what defines Canadianness is relatively tenuous one. There are few traits, markers, or qualities that are seen as characteristically Canadian. This is even more true for Canada’s culinary identity. What exactly is Canadian food? Canada, as a nation, is a relatively new country without a clear culinary identity. Further, Canada is an expansive land mass covering different time zones, geographic regions, and climates. To further complicate matters, it is place for people from all over the world to immigrate. Nowhere is the impact of immigration and the diversity of people more evident than in Toronto. How the city has changed demographically is reflected in the diversification of it’s culinary landscape. The wide range of available foods reveals and affirms how the appetites and desires of those that live here have also changed. International foods, restaurants and markets are not only ubiquitous, but a defining characteristic of the city. Where, what and how people eat can provide insight into how historical systems of inequality and colonial narratives persist. Growing and developing Canadian culinary identity is a way of challenging the idea of whiteness as a prerequisite for being Canadian. It is a potential way to acknowledge and include immigrant contributions. Food is wrapped up in politics of inequality and injustice, just as much as it is in pleasure and desire. Mapping how food is used as a tool that furthers colonization and racist dogma is key for shifting food to a tool for education and understanding. Food has the power to open up conversation and reshape understandings of Canadian identity through developing and defining a distinct Canadian culinary position. If an understanding about Canadian culinary identity is inclusive of its complex and divergent cultural and political history, then perhaps there is an opportunity to rethink Canadian identity as a whole. The goal of this MRP is to establish that food can be used as an ideological intervention that examines, challenges and reimagines Canadian identity.

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)0.009
metaresearch head score (Gemma)0.008
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 score0.103
Threshold uncertainty score0.669

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0680.050
Scholarly communication0.0200.009
Open science0.0030.013
Research integrity0.0030.008
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 head0.044
GPT teacher head0.286
Teacher spread0.242 · 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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