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Record W1995015248 · doi:10.1080/09644016.2013.818302

Understanding contemporary networks of environmental and social change: complex assemblages within Canada’s ‘food movement’

2013· article· en· W1995015248 on OpenAlexaffabout
Charles Z. Levkoe, Sarah Wakefield

Bibliographic record

VenueEnvironmental Politics · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial movementScope (computer science)Food systemsFunction (biology)PoliticsSocial network analysisIdentity (music)Environmental movementEnvironmental sociologySociologyPublic relationsPolitical scienceFood securityGeographySocial scienceSocial capital

Abstract

fetched live from OpenAlex

Emerging forms of social mobilisation are explored, using food initiatives in Canada as an example. Food networks are particularly interesting as a case study: they have holistic goals that include both environmental and social concerns, the number and scope of food initiatives have rapidly increased, and there has recently been a high level of public engagement around food issues. Networks among alternative food initiatives (AFIs) are investigated using a survey and in-depth interviews. Food movement networks exhibit some elements of collective identity, but network members have diverse goals, projects, and tactics that do not always align into a coherent political program. Social network theory and the analytic of complex assemblages are employed to help understand these results. Understanding how these food networks function provides insight not just into food networks, but also more generally into the study and practice of social mobilisation around environmental issues.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.406

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0190.022
Scholarly communication0.0130.008
Open science0.0020.007
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 head0.066
GPT teacher head0.184
Teacher spread0.117 · 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.

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

Citations58
Published2013
Admission routes2
Has abstractyes

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