Classification of High-Fatality Conflict Countries by Fragility Metrics.
Predicting the emergence of armed conflicts has long been a subject of debate in the social sciences. Numerous studies have shown that structural factors such as state capacity, economic fragility, and institutional inequality influence the risk of conflict. However, the predictive performance of th...
| Published in: | Itobiad: Journal of the Human & Social Science Researches / İnsan ve Toplum Bilimleri Araştırmaları Dergisi Vol. 15; no. 2; pp. 924 - 946 |
|---|---|
| Main Author: | |
| Format: | Article |
| Published: |
Itobiad: Journal of the Human & Social Science Researches
nissan-haziran2026
|
| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=195132237&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 195132237 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 21471185 GBL7 jtl: Itobiad: Journal of the Human & Social Science Researches / İnsan ve Toplum Bilimleri Araştırmaları Dergisi issn: 21471185 maglogo: N pubinfo: dt: nissan-haziran2026 vid: 15 iid: 2 pid: 87673 pub: Itobiad: Journal of the Human & Social Science Researches artinfo: ui: 195132237 10.15869/itobiad.1716071 ppf: 924 ppct: 22 formats: tig: atl: Classification of High-Fatality Conflict Countries by Fragility Metrics. aug: au: Akar, Çağlar affil: Assistant Professor, Istanbul Okan University Vocational School, Istanbul, Türkiye. su: War casualties Fragile States Index Predictive validity Classification algorithms sug: subj: War casualties Fragile States Index Predictive validity Classification algorithms keyword: Armed Conflict Conflict Prediction Fragility Machine Learning Poverty Kırılganlık Makine Öğrenmesi Silahlı Çatışma Silahlı Çatışma Tahmini Yoksulluk Armed Conflict Conflict Prediction Fragility Machine Learning Poverty Kırılganlık Makine Öğrenmesi Silahlı Çatışma Silahlı Çatışma Tahmini Yoksulluk ab: Predicting the emergence of armed conflicts has long been a subject of debate in the social sciences. Numerous studies have shown that structural factors such as state capacity, economic fragility, and institutional inequality influence the risk of conflict. However, the predictive performance of these indicators is often not systematically tested. This study examines the ability of structural fragility indicators to distinguish periods of high-death-toll conflict within a prospective classification framework. The analysis combines structural indicators included in the Fragile States Index with death tolls derived from ACLED event data. The dependent variable is defined as a binary indicator of whether a country experiences at least 25 conflict-related deaths in the following year. To prevent temporal information leakage and obtain a more realistic assessment, a rolling prediction design with an expanding training window was employed instead of the random data-splitting method. The Light Gradient Boosting Machine (LightGBM) algorithm, which has high capacity to capture nonlinear relationships, was preferred for classification. The findings indicate that structural fragility indicators exhibit strong discriminatory performance in identifying country-year observations that exceed the high-fatality threshold. The average ROC-AUC value in the rolling evaluation results is approximately 0.92. However, the prediction performance is not entirely constant over time. Limited weakening in both discriminatory power and probability calibration occurs in some periods. This indicates that structural indicators convey a significant signal of conflict risk, but predictive success is not independent of the temporal context. The study contributes to the conflict prediction literature by demonstrating that variables with strong explanatory power do not necessarily yield stable predictive performance over time. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|