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Record W2776271647 · doi:10.15353/cfs-rcea.v4i2.250

Sustainable Diets: How Ecological Nutrition Can Transform Consumption and the Food System by Pamela Mason and Tim Lang

2017· article· en· W2776271647 on OpenAlexaffvenue
Jennifer Sumner

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConsumption (sociology)Environmental ethicsAnthropoceneFood systemsPerspective (graphical)Function (biology)Public healthSustainable agriculturePlanetOrder (exchange)EcologyPolitical scienceSociologySustainabilityBusinessSocial scienceBiologyMedicineFood securityAgricultureComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

This path-breaking book situates the thorny issue of diets firmly within what has been called the Anthropocene—the era of human-induced changes to the planet. Since many of these changes are associated with food production and consumption, the authors argue that we need to develop sustainable diets in order to reduce the negative impacts of the current global food system on the environment, as well as to improve public health. To this end, the book is informed by an ecological public health perspective, which “views health as a function of relationships, a web of connections between humans, planet and society.”

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0050.007
Open science0.0010.002
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0070.002

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 head0.017
GPT teacher head0.209
Teacher spread0.192 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2017
Admission routes2
Has abstractyes

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