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Record W2589386070 · doi:10.20956/jars.v1i1.721

FOOD POLICY COUNCIL AS CIVIC ENGAGEMENT FOR FOOD ISSUES

2017· article· en· W2589386070 on OpenAlexaboutno aff
Masashi Tachikawa

Bibliographic record

VenueJournal of Asian Rural Studies · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceResearch Institute for Humanity and NatureMinistry of Education, Culture, Sports, Science and Technology
KeywordsFood systemsPolitical scienceGlobalizationFood policyFood securityAgricultureGeographyLaw

Abstract

fetched live from OpenAlex

The purpose of this paper is to elucidate the nature of food issue in our society and propose a forum to discuss multi-facet issues of food based on the North American experience, such as food policy council (FPC). Contemporary food system in Japan is full of problems, such as low level self-sufficiency, food loss, problem of food access, large food miles, declining food culture under globalization, and so on. After reviewing these food related issues, the paper refers to the US and Canadian experiences on food policy council as a model to provide a forum for various stakeholders with different or even conflicting interests. Based on observations on the FPCs, such as Knoxville (US) and Toronto (Canada), author emphasized public aspect of food issues and draw attentions to differences in structural aspects of food between North America and Japan. The paper also tries to draw attention to differences between North America and Japan in terms of food issues. In particular, the demographic and geographical differences would exist of a major structural aspect when considering food issue in Japan.

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)0.011
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0290.010
Scholarly communication0.0160.007
Open science0.0020.015
Research integrity0.0140.014
Insufficient payload (model declined to judge)0.0210.002

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 head0.092
GPT teacher head0.305
Teacher spread0.212 · 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
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Asian Rural Studies→Same topicOrganic Food and Agriculture→French-language works237,207→