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Record W4386086675 · doi:10.1111/joac.12560

A framework for understanding land control transfer in agricultural commodity frontiers

2023· article· en· W4386086675 on OpenAlexafffund
Olivia del Giorgio

Bibliographic record

VenueJournal of Agrarian Change · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCommodityAgricultureControl (management)Order (exchange)Space (punctuation)GlobeScale (ratio)EconomicsEconomic systemNatural resource economicsEnvironmental economicsIndustrial organizationBusinessComputer scienceMarket economyGeographyManagementFinance

Abstract

fetched live from OpenAlex

Abstract Across the globe, the expansion of large‐scale commodity agriculture is occurring not into empty space but over existing social systems. An understanding of the dynamics of expansion and associated impacts of commodity agriculture thus fundamentally requires examining how existing control regimes are dissolved and, simultaneously, how novel ones are assembled in order to make way for the changes in resources use that characterize these transitional moments. With this in mind, in this article, I provide a broad review of the strategies used to secure control over land prospected for agricultural commodity production, distinguishing between the tactics that are applied by agro‐interested actors in order to ‘break down’ forms of existing land control, those they apply in parallel to ‘build up’ new control structures, and those strategies that are applied by actors (often smallholders) wishing to ‘hold on to’ the control that they have. I then present a framework for examining the dynamics of control transfer that builds on this analytical structure of ‘breaking down’, ‘building up’, and ‘holding on to’ control.

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)0.000
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 score0.385
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.112
GPT teacher head0.257
Teacher spread0.145 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

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