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Record W1886487920 · doi:10.15353/cfs-rcea.v2i2.121

LGAR - Territorial restructuring and resistance in the Americas

2015· article· en· W1886487920 on OpenAlexvenueno aff
Zoe W. Brent

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
Fundersnot available
KeywordsLand grabbingRestructuringAgrarian societyLand reformCommunismExceptionalismAgrarian systemFinancializationRedistribution (election)Political economyResistance (ecology)Political scienceAgrarian reformContext (archaeology)Social movementSociologyEconomicsAgricultureMarket economyGeographyLawPolitics

Abstract

fetched live from OpenAlex

Over the last thirty years, social movements for agrarian reform have struggled to keep up with the profound changes in the structures of land and agricultural production sweeping the continent. In Latin America, what once was a struggle for redistribution, dignity, and social justice in the context of national liberation, has shifted towards a model of “market-led land reform” focussed on productivity, privatization and opening land markets. In the U.S., there have been some important waves of agrarian resistance, but a sense of American exceptionalism has limited agrarian reform discourse from shaping policy, especially during and after the Cold war when it became associated with communism. Today, in both the global North and South, land grabbing and the financialization of land contribute to processes of territorial restructuring and pose broad threats to rural communities, farmers, indigenous peoples, fisherfolk, farmworkers, peasants, and people of color.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.155

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 studies0.0080.014
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.051
GPT teacher head0.232
Teacher spread0.181 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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