<p>This paper addresses a significant limitation in Young’s Social Connection Model by introducing nodal structural injustice—cases where causal contributions to injustice are more clearly traceable through individual interactions. Drawing on variations of Young’s paradigmatic “Sandy” homelessness case, I distinguish pure structural injustice from nodal structural injustice, where harmful interactions, attributable wrongs, and controllable circumstances exist. I develop a novel analytical framework that synthesizes retrospective responsibility attribution with prospective counterfactual potency evaluation, integrating Bayesian reasoning with counterfactual analysis. This quantitative approach yields two significant findings: first, it enhances responsibility attribution by systematically analyzing agents’ causal contributions to injustice; second, it reveals that factors bearing highest retrospective responsibility often differ from those offering greatest preventive potential. By creating a four-part typology of responsibility-potency relationships, the framework provides policymakers with a systematic methodology for both assessing accountability and identifying optimal intervention points within complex structural systems. This approach transforms abstract theories of collective responsibility into an operational analytical tool that bridges philosophical analysis with practical policy design, offering new pathways for addressing structural injustice through both targeted interventions and broader systemic reforms.</p>

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Structural Injustice and Counterfactuals: Attributions of Responsibility

  • Jin Hu,
  • Jian Wang

摘要

This paper addresses a significant limitation in Young’s Social Connection Model by introducing nodal structural injustice—cases where causal contributions to injustice are more clearly traceable through individual interactions. Drawing on variations of Young’s paradigmatic “Sandy” homelessness case, I distinguish pure structural injustice from nodal structural injustice, where harmful interactions, attributable wrongs, and controllable circumstances exist. I develop a novel analytical framework that synthesizes retrospective responsibility attribution with prospective counterfactual potency evaluation, integrating Bayesian reasoning with counterfactual analysis. This quantitative approach yields two significant findings: first, it enhances responsibility attribution by systematically analyzing agents’ causal contributions to injustice; second, it reveals that factors bearing highest retrospective responsibility often differ from those offering greatest preventive potential. By creating a four-part typology of responsibility-potency relationships, the framework provides policymakers with a systematic methodology for both assessing accountability and identifying optimal intervention points within complex structural systems. This approach transforms abstract theories of collective responsibility into an operational analytical tool that bridges philosophical analysis with practical policy design, offering new pathways for addressing structural injustice through both targeted interventions and broader systemic reforms.