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Record W2015742826 · doi:10.1080/00045608.2014.985626

Agro-Ecology and Food Sovereignty Movements in Chile: Sociospatial Practices for Alternative Peasant Futures

2015· article· en· W2015742826 on OpenAlexaff
Beatriz Cid Aguayo, Alex Latta

Bibliographic record

VenueAnnals of the Association of American Geographers · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPeasantFood sovereigntyFutures contractPolitical ecologyEcologySovereigntyGeographyNatural resource economicsPolitical scienceEconomicsFood securityAgriculturePoliticsBiology

Abstract

fetched live from OpenAlex

The agro-ecology and food sovereignty movements of southern Chile promote alternatives to the hegemonic agro-export regime that dominates the landscape. We explore these mobilizations and the strategies they employ, with a particular focus on a network of peasant women “seed curators.” The global agri-food complex relies on a flat and universalizing spatiality of land as resource and food as commodity, in which the character and fate of individual places is of little importance. This is paired with a hierarchical monopolization of knowledge, where producers become recipients rather than creators and custodians of agricultural inputs and know-how. In response, peasant movements have given birth to alternative spatial practices based on horizontal networks that join together interdependent producers and places. By sharing traditional and agro-ecological knowledge, cultivating alternate circuits of exchange, and building urban–rural partnerships, these movements seek to reshape the horizons of possibility both for peasant communities and for the broader agri-food 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.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.013
Scholarly communication0.0060.003
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.039
GPT teacher head0.282
Teacher spread0.243 · 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

Citations27
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the Association of American GeographersSame topicAgriculture, Land Use, Rural DevelopmentFrench-language works237,207