The Entitlement Cascade and the White‑Guilt Inversion Script (WG‑IS)

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Authors

Dr Souleymane Diallo, psychiatrist
Dr Alex Johnson, data scientist
Dr Kiese Kituavewa, historian

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The Pan African Strategic Observatory

Abstract

Across Western media and platform discourse, a recurrent rhetoric appears whenever colonial violence, racial enslavement, or contemporary repair claims enter public debate: ‘enough repentance,’ ‘colonialism also brought benefits,’ ‘everyone did slavery,’ ‘white people ended slavery,’ and ‘there is too much white guilt.’ We model this cluster as a denial‑and‑domination script rather than an honest historical disagreement. Building on structural‑clinical work on perpetrator pathology and the “entitlement cascade,” we propose that the script functions as a ritualized reenactment loop that restores moral superiority, diffuses accountability, and re‑licenses extraction under stress (loss of influence, legal exposure, demographic shift, reputational risk). We integrate (i) an institutional pathology frame (DSM‑H), (ii) a leadership selection frame (Colonized Dark Tetrad / Imperial Selection Model), and (iii) a platform‑incentive frame (engagement and outrage optimization). Methodologically, we outline a preregisterable mixed‑methods program: motif coding of public discourse; a policy‑shock natural experiment around the 27 October 2022 acquisition of Twitter (now X); cross‑platform comparisons (centralized vs federated moderation); and network‑behavioral signatures of ritual reenactment (repetition, blame inversion, humiliation play, falsity load). The paper’s contribution is a falsifiable model: when accountability pressure rises, the WG‑IS intensifies, concentrates among inheritor/beneficiary clusters, and is amplified by architectures that reward provocation. We specify predictions, tests, and disconfirmations, and we supply a coding schema for replication.

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Diallo, Johnson & Kituavewa (2023). The Entitlement Cascade and the WG-IS.

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Except where otherwised noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States