MétaCan
Menu
Back to cohort
Record W2114042274 · doi:10.15353/cfs-rcea.v2i2.94

LGAR - Land grabs, the agrarian question and the corporate food regime

2015· article· en· W2114042274 on OpenAlexaffvenue
A. Haroon Akram‐Lodhi

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 institutionsTrent University
Fundersnot available
KeywordsLand grabbingAgrarian societyCapitalismScholarshipCivil societyPoliticsContext (archaeology)Political economyPolitical scienceArgument (complex analysis)SociologyLawGeographyAgricultureBiologyEcology

Abstract

fetched live from OpenAlex

Over the last decade civil society organizations and activist-scholars have pointed to “land grabbing” as one of the central issues to have emerged in the world food system. In particular, land grabbing was identified as a new and immediate international development issue by the non-governmental organization GRAIN in 2008 (www.farmlandgrab.org). Since that time land grabbing has generated a voluminous literature of a highly variable quality—some scholarship is outstanding and some is shoddy (Oya, 2013). This contribution seeks to clarify what constitutes land grabbing and why it takes place, as well as the key challenge that scholars and civil society activists face in confronting land grabbing in the context of the question of feeding the world. The central argument is that when a structuralist political economy is used to interpret the land grab phenomenon, it becomes analytically clear that contemporary land deals demonstrate that dispossession by displacement, or what has historically been known as the “so-called primitive accumulation”, has been resurrected as an accumulation strategy of global capitalism witnessing, for the first time in decades, the limits to the market. It is an accumulation strategy that cannot, however, deliver food justice or deal with the climate emergency.

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.069
GPT teacher head0.212
Teacher spread0.143 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations8
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Food Studies / La Revue canadienne des études sur l alimentationSame topicAgriculture, Land Use, Rural DevelopmentFrench-language works237,207