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Record W2011182154 · doi:10.1080/13549839.2014.908277

Neoliberalism and the making of food politics in Eastern Ontario

2014· article· en· W2011182154 on OpenAlexaffabout
Peter Andrée, Patricia Ballamingie, Brynne Sinclair-Waters

Bibliographic record

VenueLocal Environment · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsCarleton University
Fundersnot available
KeywordsNeoliberalism (international relations)Transformative learningOpposition (politics)Food systemsPoliticsPolitical economyArgument (complex analysis)SociologyState (computer science)Political scienceEnvironmental ethicsFood securityLawGeography

Abstract

fetched live from OpenAlex

Despite their opposition to the dominant agri-food system, alternative agri-food initiatives may unwittingly reproduce central features of neoliberalism. Julie Guthman has been a particularly strong proponent of this view, arguing that food activism and neoliberalism have shaped one another dialectically in California in recent decades. This paper responds to her argument, with a view to distinguishing between what it reveals and what it may conceal about the transformative potential of alternative agri-food initiatives in North America. Drawing on primary research on a variety of community-based food initiatives in Eastern Ontario, Canada, we show how a neoliberal lens does help to illuminate some problematic characteristics of these initiatives, including assumptions about market-based solutions and focus on self-improvement at the expense of state involvement. However, this lens underestimates those aspects of community-based food initiatives that may appear commensurate with neoliberal rationalities but which also push in more progressive directions.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.996

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0220.012
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.157
Teacher spread0.149 · 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

Citations44
Published2014
Admission routes2
Has abstractyes

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