A Structural Model of Classic Film Noir Within the SLP Framework: A Systematic Content Analysis of the Interaction Among Visual, Narrative, and Character Structures - Hashemi, Seyed Amirreza; Shahghasemi, Ehsan; Hosseini, Mohammad Reza (AI and Tech in Behavioral and Social Sciences, 2026)
Classic film noir is one of the most influential stylistic movements in the history of cinema, distinguished by visual darkness, complex narrative structures, and morally ambiguous characters. Although this genre has been widely examined, much existing scholarship has treated its visual, narrative, and character dimensions separately, with limited attention to the systematic interaction among these structural levels. This study proposes a preliminary structural model of classic film noir within the SLP framework and examines the co-occurrence of visual style, narrative logic, and characterization. Methodologically, the study adopts a quantitative-analytical design based on systematic content analysis. The research population consisted of classic American film noir productions from the 1940s and 1950s, from which 100 prominent films were selected through purposive sampling. Data were collected through direct observation of the films and binary coding of indicators related to visual style, narrative logic, and characterization. Intercoder reliability was assessed using Cohen's kappa coefficient (κ = .81). The findings showed that characterization indicators had the highest mean presence (66.2%), followed by visual style (63.7%) and narrative logic (60.6%). High-contrast lighting, narrative suspense, and the presence of the antihero were among the most frequently observed indicators. Phi correlation analysis indicated that several pre-defined indicators appeared in clustered co-occurrence patterns. These results suggest that classic film noir may be understood as a multilayered system shaped by the association of visual, narrative, and character elements. The proposed model is therefore interpreted as an exploratory content-analysis model rather than as a fully validated causal or computational model.