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Record W2003799357 · doi:10.1111/1468-0130.00267

Land Tenure and Legal Pluralism in the Peace Process

2003· article· en· W2003799357 on OpenAlexaff
Jon D. Unruh

Bibliographic record

VenuePeace &amp Change · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsMcGill University
Fundersnot available
KeywordsLegal pluralismLand tenureNormativeProcess (computing)Armed conflictPluralism (philosophy)Property rightsProperty (philosophy)Political scienceLaw and economicsSociologyLawSocial conflictConflict resolutionPoliticsLegal researchLegal realismEpistemologyGeography

Abstract

fetched live from OpenAlex

Land tenure has proven to be one of the most vexing issues in a peace process. The disintegration of land and property rights institutions during armed conflict yet the importance of land and property to the conduct of conflict present particular dilemmas for a peace process attempting to reconfigure aspects of societal relations important to recovery. In this regard understanding what happens to land tenure as a set of social relations during and subsequent to armed conflict is important to the derivation of useful tools for managing tenure issues in a peace process. This article examines the development of multiple, informal “normative orders” regarding land tenure during armed conflict and how these are brought together in problematic form in a peace process. While there can be significant development of tenurial legal pluralism during armed conflict, it is during a peace process that problems associated with different approaches to land claim, access, use, and disputing become especially acute, because an end to hostilities drives land issues to the fore for large numbers of people over a short time frame.

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)0.011
metaresearch head score (Gemma)0.011
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 score0.015
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.063
Scholarly communication0.0120.010
Open science0.0010.011
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.040
GPT teacher head0.245
Teacher spread0.205 · 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

Citations77
Published2003
Admission routes1
Has abstractyes

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