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Record W4386457941 · doi:10.1007/s10460-023-10501-y

Biotechnology activism is dead; long live biotechnology activism! The lure and legacy of market-based food movement strategies

2023· article· en· W4386457941 on OpenAlexafffund
Gabriela Pechlaner

Bibliographic record

VenueAgriculture and Human Values · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of the Fraser Valley
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTransformative learningCredenceCounterintuitiveFood systemsValue (mathematics)Political sciencePublic relationsEnvironmental ethicsSociologyBiologyFood securityEcology

Abstract

fetched live from OpenAlex

Scholarly debate over the transformative potential of neoliberal, market-based, food movement strategies historically contrasts those who value their potential to reform the food-system from the inside against those who argue that their use concedes the primacy of the market, creates citizen-consumers, and undermines overall movement goals. While narrow case studies have provided important amendments, the legacy of such strategies requires impacts to be evaluated both contextually and more broadly than the specific activism. This study thus conceptualizes the 'case' of U.S. biotechnology market activism expansively, drawing on interviews with 25 activists from diverse organizations to investigate the legacy of two food-labeling movement strategies (one public and mandatory, one private and voluntary). The results support that the legacy of market strategies extends more broadly than the immediate initiative. They also confirm that the consequences of such neoliberalized strategies are most productively assessed contextually and applied, rather than categorically-as most clearly illustrated by the counterintuitive results of the failed mandatory labeling effort. Of the two market strategies, voluntary labeling demonstrated the most problematic relationship to broader movement goals of food system transformation, in part because of the greater potential for overlapping credence claims and in part due to the risks of niche market logic.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.216
Teacher spread0.203 · 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 designBench or experimental
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

Citations4
Published2023
Admission routes2
Has abstractyes

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