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Record W4394686853 · doi:10.1080/08865655.2024.2338754

Landmines and Human Security in Post-Conflict Era: Analyzing the Narratives of Landmine Victims in Kurdistan Province, Iran

2024· article· en· W4394686853 on OpenAlexvenueno aff
Parviz Sobhani, Hussein Daneshmehr

Bibliographic record

VenueJournal of Borderlands Studies · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeHuman securityPolitical scienceGeographyLawLiteratureArt

Abstract

fetched live from OpenAlex

The border areas of Kurdistan Province in western Iran continue to be plagued by the presence of landmines left over from the Iran-Iraq war, causing frequent accidents and inflicting severe physical, mental, and social damage on the residents. This paper aims to explore the lived experiences of landmine explosion victims in this region. The immediate aftermath of landmine explosions in these borderlands often results in the amputation of vital organs or tragic fatalities. Survivors of these incidents endure various forms of social and psychological trauma due to their physical disabilities. However, the provision of necessary services to these victims is characterized by a dualistic approach, where some receive support while others are deprived of assistance in order to facilitate activities such as smuggling, kolbariFootnote1, and illegal trafficking. This structural disparity exacerbates the challenges faced by victims, particularly those residing in border areas.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.011
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0020.003
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 head0.028
GPT teacher head0.372
Teacher spread0.344 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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