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Record W1976955164 · doi:10.15353/cfs-rcea.v1i1.43

Voices and visuals from the Canadian foodscape

2014· article· en· W1976955164 on OpenAlexaffvenueabout
Ellen Desjardins

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsContext (archaeology)Library sciencePower (physics)Table of contentsMedia studiesPolitical scienceSociologyPublic relationsHistory

Abstract

fetched live from OpenAlex

Welcome to the inaugural issue of Canadian Food Studies/La Revue canadienne des études sur l’alimentation, the open-access, online journal of the Canadian Association for Food Studies/l’Association canadienne des études sur l’alimentation (CAFS/ACÉA). Our journal arrives on the scene in the context of a well-established organization that has, for the past decade, promoted multidisciplinary interaction, debate, and research about food in Canada. The prolific and diverse nature of this food-related research was hailed earlier by Power & Koç (2008) in a guest-edited collection of articles by Canadian scholars to celebrate the founding of CAFS. Indeed, every year since 2006, the CAFS meeting at the Congress of the Humanities and Social Sciences has served as a communal table, laden with a smorgasbord of collaborative and transformational ideas. The feast continues, not just among academic faculty and students, but shared with health professionals, artists, teachers, farmers, fishers, environmentalists, chefs, and colleagues from NGOs, food networks and policy councils. Collectively, these actors form the voices and visuals of the Canadian foodscape. The purpose of our journal is to offer a professional, academic forum for the astonishing breadth and depth of material that they can produce.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.229
Teacher spread0.190 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2014
Admission routes3
Has abstractyes

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