MétaCan
Menu
Back to cohort
Record W2518409997 · doi:10.1111/joac.12182

Repossession, Re‐informalization and Dispossession: The ‘Muddy Terrain’ of Land Commodification in Turkey

2016· article· en· W2518409997 on OpenAlexaff
Yıldı‌z Atasoy

Bibliographic record

VenueJournal of Agrarian Change · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCommodificationLand titlingLand grabbingCommercializationCadastreLand registrationLand reformModernization theoryEconomic growthLand tenureEconomyPolitical scienceAgricultureGeographyEconomicsArchaeologyLaw

Abstract

fetched live from OpenAlex

This paper examines the process of land commodification in the commercialization of agriculture and housing in Turkey. Specific mechanisms involved include cadastre modernization, land titling, land registration and land‐consolidation schemes. Through these techniques, the state increases its control over common‐public lands, reconfigures land‐use and access patterns, and deepens commodification. The paper traces historical variation in land use from the national developmentalist to the neoliberal phases of capital accumulation in Turkey, with comparative, contextual examples drawn from the Ottoman Empire. It highlights the combined and socio‐spatially differentiated processes of commodification across sectors that engender a multiplicity of outcomes in simultaneously framing commercialization of agriculture and housing. Contextual analysis of official documents and histories is complemented by information gathered from fieldwork and in‐depth interviews in several former wheat‐cultivating villages, a former gecekondu neighbourhood, and a small agricultural town in the province of Ankara.

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)0.001
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 score0.014
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.008
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.233
Teacher spread0.196 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agrarian ChangeSame topicAgriculture, Land Use, Rural DevelopmentFrench-language works237,207