MétaCan
Menu
Back to cohort
Record W2765151459 · doi:10.3390/su9111981

Confusion and Misunderstanding—Interpretations and Definitions of Local Food

2017· article· en· W2765151459 on OpenAlexfundno aff
Madeleine Granvik, Sofie Joosse, Alan Hunt, Ingela Hallberg

Bibliographic record

VenueSustainability · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
FundersMinistry of Rural AffairsSveriges Lantbruksuniversitet
KeywordsFood securityConsumption (sociology)Context (archaeology)Food processingMeaning (existential)Food systemsProduction (economics)ConfusionBusinessMarketingFood chainSustainabilityPolitical scienceSociologyAgricultureEconomicsGeographySocial sciencePsychology

Abstract

fetched live from OpenAlex

Developing a more resilient food system based on sustainable food production and consumption is of major concern in creating food security. One issue in this complex field concerns the scale of the food system. Trends and tendencies show that the interest for local food has increased the last decade in Sweden, as well as in other parts of the world. Although the concept “local food” is commonly used, research shows that there is no single definition of it, instead definitions and meanings vary widely. This has led to a need by consumers of clearer information when buying “local food”. Several main actors in the Swedish food sector have joined forces to meet this issue. This paper contributes to knowledge on definitions, interpretations, and practice on local food by presenting views and opinions among different actors in the food chain in a Swedish context, but also in the light of an international pilot study. Main findings concern how the meaning of “local food” related to production, processing, raw material, and distance differs among stakeholders in the food chain. A majority stated that the basic meaning of “local food” concerns both the production and consumption within a certain geographical area.

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 score0.685
Threshold uncertainty score0.375

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 studies0.0000.000
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 head0.024
GPT teacher head0.230
Teacher spread0.206 · 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

Citations44
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicOrganic Food and AgricultureFrench-language works237,207