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Record W7165572961 · doi:10.48416/ijsaf.v31i1.759

Organic Carrots and Conventional Sticks

2025· article· en· W7165572961 on OpenAlexaff
Marylynn Steckley, Joshua Steckley

Bibliographic record

VenueInternational Journal of Sociology of Agriculture and Food · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsCarleton University
Fundersnot available
KeywordsOrganic farmingAgricultureCommodificationSAFERSustainable agricultureFood systemsProduction (economics)Organic product

Abstract

fetched live from OpenAlex

The organic label is the most recognised food label in the world, and consumers perceive organic agriculture as healthier, safer and more sustainable than conventional agriculture. Yet scholars show that both organic and conventional farming systems are diverse, and the purported benefits of organic agriculture are not at all straightforward. At the grocery store, consumers have two options: organic or not. In this paper, we argue that this is a false dichotomy with concerning social and environmental consequences. Processes of commodification obscure the diverse social and ecological conditions under which both organic foods and conventional foods are produced, ultimately positioning ‘conventional’ production systems and foods as ‘other’. This has important implications for conventional agriculture, potentially disincentivising and demotivating producers from adopting environmentally and socially enriching farm management practices.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.018
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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 designQualitative
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
Published2025
Admission routes1
Has abstractyes

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