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Record W4387679615 · doi:10.4000/communiquer.10819

Quand les entreprises agroalimentaires « végétalisent » leur offre

2023· article· fr· W4387679615 on OpenAlexvenueno aff
Amélie Aubert-Plard, Titouan Bénégui

Bibliographic record

VenueCommuniquer Revue de communication sociale et publique · 2023
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Face à l’enjeu sociétal de la transition écologique et alimentaire, cet article cherche à comprendre dans quelle mesure les citoyen·ne·s perçoivent les entreprises agroalimentaires comme des actrices du changement, et la consommation de leurs nouvelles offres végétales comme un moyen d’y participer. Il mobilise les théories de la socio-anthropologie de l’alimentation et s’appuie sur une enquête qualitative menée en 2021 en Île-de-France. L’article propose en premier lieu une typologie des différentes attitudes des citoyen·ne·s face à cet enjeu sociétal. Celle-ci permet de mettre en lumière deux leviers principaux d’engagement des citoyen·ne·s, à savoir la prise de conscience issue d’une expérience personnelle et empirique et la transmission d’informations par le réseau interpersonnel. Grâce à la typologie, l’article analyse ensuite les différentes réactions des citoyen·ne·s face aux offres végétales industrielles et à la communication associée des entreprises. Enfin, il s’interroge sur les efforts d’innovation poursuivis par les industries au regard des demandes des citoyen·ne·s en termes de transparence et d’information.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.091
GPT teacher head0.321
Teacher spread0.230 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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