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Record W3009304252 · doi:10.1002/pan3.10075

Farming along desire lines: Collective action and food systems adaptation to climate change

2020· article· en· W3009304252 on OpenAlex
Bernard Soubry, Kate Sherren, Thomas F. Thornton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePeople and Nature · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCollective actionAdaptation (eye)Food systemsPsychological resilienceAgency (philosophy)Action (physics)Corporate governanceSocial capitalResilience (materials science)MetaphorAgricultureBusinessClimate changeAdaptive capacityEnvironmental resource managementSociologyPolitical scienceEconomicsFood securityPsychologySocial psychologyGeographyEcologySocial science

Abstract

fetched live from OpenAlex

Abstract We examine collective action in the food system of the Canadian Maritimes to determine its effect on the resilience and adaptive capacity of food producers, distributors, retailers and governance institutions. Our data suggest that beyond their immediate benefits for their participants, expressions of collective action generate higher‐level impacts which often translate into drivers of adaptive capacity. Drawing on a metaphor from urban design, we suggest that collective action should be considered a desire line for food systems adaptation: rather than building adaptation strategies based on top‐down design, collective action emerges from farmers’ needs and capacities to build financial resilience, enhance human and social capital and strengthen institutional agency within the system.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.240
Teacher spread0.215 · 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