LLM + Neutrosophic fsQCA: Inter-Narrative Causal Consistency and Paraconsistent Detection in Media Accounts of Urban Violence in Guayaquil

Maikel Leyva, Noel Batista and Florentin Smarandache

No yxdsb_v1, SocArXiv from Center for Open Science

Abstract: Urban violence in Guayaquil, Ecuador has reached crisis levels, yet causal explanations remain fragmented across local and international media. This paper introduces N-fsQCA, a Neutrosophic extension of fuzzy-set Qualitative Comparative Analysis (fsQCA), and combines it with Large Language Models (LLMs) to extract and compare causal narratives from a bilingual corpus of 31 sources (16 local Spanish-language, 15 international English-language). Four LLMs (Google Gemini-2.0-Flash-Lite, Meta LLaMA-3.1-8B, Microsoft Phi-4, Qwen-3-8B) assign fuzzy scores [0,1] to eight causal conditions; indeterminacy (I) is operationalized as inter-LLM variance (Var/0.25), capturing epistemic disagreement as structural information. Results show: (i) the configuration territorial_war * prison_linkages achieves the highest consistency (T=0.908, I=0.092); (ii) international media emphasizes drug routes (gap=+0.330) and prison linkages (gap=+0.309) at roughly double the rate of local media; (iii) five structural drivers are systematically silenced in the press, with weapons trafficking showing the largest gap (-0.444) against a structural baseline derived from a validated perception survey (n=179) and grey literature. The pipeline is open-source (MIT License).

Date: 2026-05-07
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:yxdsb_v1

DOI: 10.31219/osf.io/yxdsb_v1

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