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Record W2995503933 · doi:10.1080/08865655.2019.1700388

The Impact of Concrete Wall Construction Between Iran and Afghanistan Border on Safety of Iran South-Eastern Marginal Regions

2019· article· en· W2995503933 on OpenAlexvenueno aff
S Ghanbari, Omid Jamshidzehi Shahbakhsh, Mahdi Naderianfar

Bibliographic record

VenueJournal of Borderlands Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyStatistical softwareTerrorismDescriptive statisticsMiddle EastSocioeconomicsStatisticsMathematicsEconomics

Abstract

fetched live from OpenAlex

Being located in the Middle East region, Iran is encountering fundamental challenges with regard to its safety. Loss of peace and safety in Afghanistan, existence of drug smuggling bands, weapons and entrance of terrorist troops from Afghanistan to Iran are the factors that continuously have endangered the security stability of Iran south-eastern marginal regions. In April 2009, the security wall construction plan between Sistan and Afghanistan common border was confirmed, and it was being fenced by military forces. Basically, the present research was intended to analyze marginal wall construction effects on safety of Sistan rural regions. To this end, a descriptive-analytical method was used based on secondary data, field studies and surveys. Size of the statistical society was determined as 375 households in 30 sample villages by considering the number of households in the villages using the Cochran formula. To analyze data ArcGIS software for spatial analysis, statistical methods of Wilcoxon in SPSS software were used in order to analyze changes in the security of the villages before and after wall construction. The research results indicated that concrete wall construction in Iran and Afghanistan border could lead to sustainable safety increment in villages of the Sistan region.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.041
GPT teacher head0.351
Teacher spread0.311 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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