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Record W2067509078 · doi:10.1177/146499341101200103

A volatile interaction between peacebuilding priorities: road infrastructure (re)construction and land rights in Afghanistan

2012· article· en· W2067509078 on OpenAlexaff
Jon D. Unruh, Mourad Shalaby

Bibliographic record

VenueProgress in Development Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsMcGill University
Fundersnot available
KeywordsPeacebuildingInsurgencyContext (archaeology)Language changeGovernment (linguistics)Land usePolitical scienceEnvironmental planningPoliticsPublic administrationLawCivil engineeringEngineeringGeography

Abstract

fetched live from OpenAlex

The current approach to peacebuilding is to focus on the specific building blocks of the process. However, such attention and building blocks are to date largely isolated from each other in their planning, analysis, implementation and measures for success with regard to contributing to overall peace. While two of these, land rights and road infrastructure, are regarded separately as crucial to post-war recovery, their interaction has not yet been examined. This article looks at these two priorities for Afghanistan, and finds in their interaction a large and acute problem of land seizures which the government and the international community in-country are unable to manage. This land grabbing is a direct result of a context of pervasive corruption, ongoing conflict, a mistaken understanding of the nature of the benefits of road reconstruction, large-scale dislocation and widespread use of explosive devices. Such a pervasive problem sets back recovery, detracts from durable peace and fuels the insurgency.

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)0.002
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.355
Teacher spread0.315 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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