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Quadratic Biological Compounding of Intergenerational Trauma Under Sustained Discrimination: A Cross-Population Framework
Abstract
The relationship between sustained cultural discrimination and biological harm across generations has been documented in multiple research domains, yet lacks a unifying theoretical framework. This paper proposes a quadratic response model in which sustained cultural discrimination creates biological impacts that compound exponentially across generations through the interaction between inherited epigenetic vulnerability and current exposure. The framework synthesizes Dr. Rachel Yehuda's epigenetics research on Holocaust survivors, Dr. Ilan Meyer's minority stress model, and trauma biology literature into a predictive model with universal application. I present seven testable predictions with clear falsification criteria, cross-population measurement protocols spanning five distinct marginalized groups, and potential experimental validation approaches. The framework explicitly addresses structural power mechanisms driving cultural force, distinguishes among prenatal, postnatal, and ancestral transmission pathways, and provides a dual-line model encompassing both victim and perpetrator intergenerational patterns. All proposed research will be conducted under Community-Based Participatory Research (CBPR) principles with explicit community partnerships. This framework emerges from both systems architecture methodology applied to social-biological phenomena and direct lived experience as a member of a Holocaust survivor family, offering a perspective that bridges technical rigor with embodied understanding.
The Tesseract of Antisemitism: A Hybrid Geometric-Causal Framework for Modeling Persistence and Amplification — Methodological Proposal & Pre-Analysis Plan (PAP)
Abstract
Antisemitism rose sharply after October 7, 2023 across multiple regions and platforms. We propose the Tesseract Framework—a hybrid model that (i) represents antisemitism as a four-dimensional system (Institutional/Scriptural Priming IP, Intergenerational Stress IS, Community Response CR, Digital Amplification DA) and (ii) tests causal pathways via a temporal transmission cascade (IP→IS→CR→DA→Incidents), supplemented by machine-learning surge forecasting. This paper is a methodological proposal with a pre-analysis plan. All effect sizes and performance metrics reported herein are simulation-based, calibrated to published aggregates (ADL, CST, EU-FRA, NCRI/CyberWell; Yehuda et al.). We specify constructs, measurement, identification strategies (SEM with lagged structure; IV for DA), and robustness checks, and we preregister falsifiable predictions and decision rules. Upon data access (panel, incident, and digital-trace sources outlined), we will replace simulated quantities with empirical estimates and update the preregistration. Expected performance under realistic data-generating processes: Institutional Priming (binary dual-scriptural encoding) predicts +98% baseline incidents (target β = 0.62) Intergenerational Stress mediates ~26% of priming → response (indirect = 0.21, 95% CI [0.14, 0.28]) Digital Amplification causes +72% velocity (IV estimate) ML forecast achieves RMSE = 11.4, AUC = 0.91 for surge detection Five pre-registered predictions specified. Implications: Prioritize platform reforms (high leverage via synergies).
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