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Record W7039800790

Nature's Past Episode 037: Histories of Canadian Environmental Issues, Part VII – Agri-Food Systems, II

2013· other· en· W7039800790 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2013
Typeother
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
Fundersnot available
KeywordsFood studiesAgricultureFood systemsPleasureCultural historyFood processing
DOInot available

Abstract

fetched live from OpenAlex

The history of Canadian food and agriculture is an enormous topic with both a global and deeply personal scope. All humans require food to live and agricultural products become food for our consumption, demonstrating the profound interrelatedness of food and agriculture. Beyond sheer survival, food serves social and cultural purposes for all people, from planting and harvesting, through preparation, and ultimately with consumption. Communities and families coalesce around these activities and have done so for all of human existence. Food is a source of pleasure and for many people is intricately linked with spirituality. Examining the environmental history of food and agriculture in Canada reveals the ways in which our complex relationships with nature and each other inform this most intimate aspect of our daily lives.
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\nOn this second part of our look at agri-food systems in Canadian history, we discuss Canadian food history and we speak with the editors and authors of a new anthology from University of Toronto Press called, Edible Histories, Cultural Politics: Towards a Canadian Food History. This round table interview features Franca Iacovetta, Valerie Korinek, Marlene Epp, James Murton, and Ian Mosby.
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\nBook cover for Edible Histories, Cultural Politics: Towards a Canadian Food History
\nPlease be sure to take a moment to fill out a short listener survey here.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.165
Teacher spread0.155 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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