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Record W2044776555 · doi:10.1080/14747731.2014.887388

Governing the Global Land Grab: What role for Gender in the Voluntary Guidelines and the Principles for Responsible Investment?

2014· article· en· W2044776555 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGlobalizations · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of CanadaInternational Fund for Agricultural Development
KeywordsCorporate governanceScholarshipLand grabbingWarrantPoliticsInequalityInvestment (military)Arable landPolitical scienceEconomicsPolitical economyEconomic growthAgricultureFinanceGeographyLaw

Abstract

fetched live from OpenAlex

The heightened interest in large-scale foreign agricultural investment in regions with ‘unused’ arable land has triggered a great deal of international attention. Concerns about ‘land grabbing’ have initiated efforts at the global level to establish standards for ‘responsible investment’ and good governance. These initiatives warrant critical examination given the social, political, and economic inequalities to which they are designed to respond, yet the scholarship on these initiatives frequently fails to incorporate gendered analyses. This article argues that gendered analysis of the governance of land grabs not only belongs at the local level—where it continues to yield important insights into how gender inequality is manifested in various forms of local governance—but that it is sorely needed at the global level as well. As such, this article begins an assessment of these governance frameworks and how they consider local realities, with particular attention to gender-based inequalities.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.461

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 head0.050
GPT teacher head0.265
Teacher spread0.214 · 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