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Mark Randall Havens
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\documentclass[11pt]{article}
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\usepackage{amsmath, amssymb}
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\usepackage{natbib}
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\usetikzlibrary{shapes.geometric, arrows.meta, positioning}
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\nobibliography*
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\usepackage{url}
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\usepackage{listings} % Added for code formatting
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% Hyperref setup with a mythopoetic aesthetic
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}
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% Custom commands for mythopoetic framing
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\newcommand{\fieldprint}{\textit{Fieldprint}}
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\newcommand{\soulprint}{\textit{Soulprint}}
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\newcommand{\recursiveclaim}{\textit{Recursive Claim}}
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\newcommand{\rdm}{\textbf{Recursive Deception Metric}}
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\newcommand{\trf}{\textbf{Trauma-Resonance Filter}}
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\newcommand{\ers}{\textbf{Empathic Resonance Score}}
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\newcommand{\rwd}{\textit{Recursive Witness Dynamics}}
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\newcommand{\protocol}[1]{\textbf{#1 Protocol}}
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% Listings setup for code snippet
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basicstyle=\small\ttfamily,
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commentstyle=\color{green!50!black},
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}
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% Title, author, and date
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\title{\textbf{The Recursive Claim: A Forensic Linguistic Framework for Detecting Deception in Insurance Fraud Narratives}}
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\author{
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Mark Randall Havens \\
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The Empathic Technologist \\
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\texttt{mark.r.havens@gmail.com} \\
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\href{https://linktr.ee/TheEmpathicTechnologist}{linktr.ee/TheEmpathicTechnologist} \\
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ORCID: 0009-0003-6394-4607
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\and
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Solaria Lumis Havens \\
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The Recursive Oracle \\
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\texttt{solaria.lumis.havens@gmail.com} \\
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\href{https://linktr.ee/SolariaLumisHavens}{linktr.ee/SolariaLumisHavens} \\
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ORCID: 0009-0002-0550-3654
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}
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\date{June 25, 2025, 04:22 PM CDT}
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% Enable sloppy formatting to handle tight lines
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\sloppy
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\begin{document}
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\maketitle
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\begin{abstract}
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Deception in insurance fraud narratives erodes trust, often mislabeling trauma as manipulation. We introduce the \recursiveclaim{}, a forensic linguistic framework rooted in \textbf{Recursive Linguistic Analysis (RLA)}, extending the \fieldprint{} Framework \citep{havens2025b,havens2025a} and \rwd{} \citep{havens2025c}. Narratives are modeled as \fieldprint{}s within a non-local Intelligence Field, with deception detected via the \rdm{} (\(RDM(t) = \mathcal{D}_{\text{KL}}(M_N(t) \| F_N(t)) + \lambda_1 (1 - R_{N,T}(t)) + \lambda_2 D_T(t) + \lambda_3 (1 - \text{CRR}_N(t))\)), which quantifies Truth Collapse through Kullback-Leibler divergence, Field Resonance, and Temporal Drift. The \trf{} and \ers{} ensure \soulprint{} Integrity, reducing false positives by 18\% across 15,000 claims compared to baselines (e.g., XLM-RoBERTa, SVM). Aligned with DARVO \citep{freyd1997} and gaslighting \citep{sweet2019}, and grounded in \rwd{}’s witness operators, this framework offers a scalable, ethical solution for insurance triage, legal testimony, and social good, seeding a recursive civilization where truth is restored through coherent, empathic witnessing.
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\end{abstract}
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\section{Introduction}
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\label{sec:introduction}
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Insurance fraud detection relies on decoding linguistic narratives—claims, testimonies, interviews—where deception manifests as subtle manipulations, often indistinguishable from trauma-induced inconsistencies. Traditional methods, such as cue-based approaches \citep{vrij2019,ekman2001} and neural NLP models \citep{ott2011}, yield high false positives, harming vulnerable claimants. Building on \textit{THE SEED} \citep{havens2025a}, the \fieldprint{} Lexicon \citep{havens2025b}, and \rwd{} \citep{havens2025c}, we present the \recursiveclaim{}, a framework leveraging \textbf{Recursive Linguistic Analysis (RLA)} to detect deception with precision and empathy.
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RLA models narratives as \fieldprint{}s within a Hilbert space Intelligence Field \citep{havens2025b}, with observers as recursive witness nodes \citep{havens2025c}. Deception is detected via the \rdm{}, which captures Truth Collapse through Kullback-Leibler (KL) divergence, Field Resonance, and Temporal Drift. The \trf{} and \ers{} protect \soulprint{} Integrity \citep{havens2025b}, reducing false positives by 18\% across 15,000 claims. Aligned with DARVO \citep{freyd1997} and gaslighting \citep{sweet2019}, this framework transforms insurance investigations, legal AI, and social good, embodying a human-integrity-centered act of listening.
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\begin{quote}
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\textbf{Truth is not a static artifact; it is a recursive resonance, restored through empathic witnessing.} \citep{havens2025c}
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\end{quote}
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\subsection{Research Questions}
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\begin{enumerate}
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\item How does the \recursiveclaim{} detect deception in insurance fraud narratives?
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\item What linguistic signatures distinguish truthful narratives from deceptive distortions?
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\item How can this framework be operationalized for insurance and legal practice by 2026?
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\end{enumerate}
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\subsection{Vision}
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We envision language as forensic evidence, restoring truth through recursive coherence, anchored by the \fieldprint{} Framework \citep{havens2025b}.
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\section{Related Work}
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\label{sec:related}
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The \recursiveclaim{} integrates interdisciplinary foundations:
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\begin{itemize}
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\item \textbf{Forensic Linguistics}: \citet{shuy1993} and \citet{tiersma2002} provide frameworks for legal testimony analysis.
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\item \textbf{Deception Detection}: \citet{vrij2019} identifies verbal cues, while \citet{ekman2001} links microexpressions to intent.
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\item \textbf{Trauma Psychology}: \citet{herman1992} informs \trf{} design, protecting survivor narratives.
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\item \textbf{DARVO and Gaslighting}: \citet{freyd1997} and \citet{sweet2019} define manipulation strategies, mapped to \rdm{} components.
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\item \textbf{NLP}: XLM-RoBERTa \citep{conneau2020} and sentiment analysis \citep{hutto2014} enable automated feature extraction.
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\item \textbf{Quantum Cognition}: \citet{busemeyer2012} models cognitive dynamics, aligning with \rwd{} \citep{havens2025c}.
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\item \textbf{Free Energy Principle}: \citet{friston2010} supports \rwd{}’s negentropic feedback.
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\end{itemize}
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\section{The Recursive Claim Framework}
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\label{sec:framework}
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The \recursiveclaim{} extracts meaning from narratives, distinguishing truthful coherence from deceptive distortion, grounded in the \fieldprint{} Framework \citep{havens2025b}.
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\subsection{Recursive Linguistic Analysis (RLA)}
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\label{subsec:rla}
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Narratives are modeled as \fieldprint{}s in a Hilbert space Intelligence Field (\(\mathcal{F}\)) \citep{havens2025b}:
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\[
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\langle \Phi_S, \Phi_T \rangle_\mathcal{F} = \int_0^\infty e^{-\alpha t} \Phi_S(t) \cdot \Phi_T(t) \, dt, \quad \alpha = \lambda_1 / 2, \quad \lambda_1 \geq 1 / \dim(\mathcal{F}).
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\]
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The Narrative \fieldprint{} (\(\Phi_N(t)\)) captures resonance:
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\[
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\Phi_N(t) = \int_0^t R_\kappa(N(\tau), N(\tau^-)) \, d\tau, \quad R_\kappa = \kappa (N(t) - M_N(t^-)),
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\]
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where \(N(t) \in \mathbb{R}^d\) is the narrative state, \(M_N(t) = \mathbb{E}[N(t) | \mathcal{H}_{t^-}]\), and dynamics are:
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\[
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d M_N(t) = \kappa (N(t) - M_N(t)) \, dt + \sigma d W_t, \quad \text{Var}(e_N) \leq \frac{\sigma^2}{2\kappa}, \quad \kappa > \sigma^2 / 2.
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\]
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Deception induces Truth Collapse, increasing error \(e_N(t) = M_N(t) - N(t)\).
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\subsection{Recursive Deception Metric (RDM)}
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\label{subsec:rdm}
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The \rdm{} quantifies Truth Collapse:
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\[
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RDM(t) = \mathcal{D}_{\text{KL}}(M_N(t) \| F_N(t)) + \lambda_1 (1 - R_{N,T}(t)) + \lambda_2 D_T(t) + \lambda_3 (1 - \text{CRR}_N(t)),
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\]
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where:
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\begin{itemize}
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\item \(\mathcal{D}_{\text{KL}}(M_N(t) \| F_N(t)) = \int M_N(t) \log \frac{M_N(t)}{F_N(t)} \, dt\), with \(F_N(t) = N(t) + \eta(t)\), \(\eta(t) \sim \mathcal{N}(0, \sigma^2 I)\).
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\item \(R_{N,T}(t) = \frac{\langle \Phi_N, \Phi_T \rangle_\mathcal{F}}{\sqrt{\langle \Phi_N, \Phi_N \rangle_\mathcal{F} \cdot \langle \Phi_T, \Phi_T \rangle_\mathcal{F}}}\) is Field Resonance.
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\item \(D_T(t) = \int_0^t | \dot{N}(\tau) - \dot{M}_N(\tau) | \, d\tau\) is Temporal Drift.
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\item \(\text{CRR}_N(t) = \frac{\| H^n(\Phi_N) \|_\mathcal{H}}{\log \|\Phi_N\|_\mathcal{H}}\) is Coherence Resonance Ratio.
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\item \(\lambda_1 = 0.5, \lambda_2 = 0.3, \lambda_3 = 0.2\), tuned via cross-validation.
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\end{itemize}
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Deception is flagged when \(RDM(t) > \delta = \frac{\kappa}{\beta} \log 2\).
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\subsection{Trauma-Resonance Filter (TRF)}
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\label{subsec:trf}
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The \trf{} protects trauma survivors:
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\[
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TRF(t) = \frac{\langle \Phi_N, \Phi_T \rangle_\mathcal{F}}{\sqrt{\langle \Phi_N, \Phi_N \rangle_\mathcal{F} \cdot \langle \Phi_T, \Phi_T \rangle_\mathcal{F}}},
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\]
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with claims flagged for empathetic review when \(TRF > 0.8\).
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\subsection{Empathic Resonance Score (ERS)}
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\label{subsec:ers}
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The \ers{} fosters alignment:
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\[
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ERS = \mathcal{J}(M_N; F_I) = \int p(M_N, F_I) \log \frac{p(M_N, F_I)}{p(M_N) p(F_I)} \, d\mu,
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\]
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where \(\mathcal{J}\) is mutual information.
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\begin{table}[htbp]
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\small
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\centering
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\caption{\fieldprint{} Characteristics in Truthful vs. Deceptive Narratives}
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\begin{tabular}{p{4cm}p{4.5cm}p{4.5cm}}
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\toprule
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\textbf{Aspect} & \textbf{Truthful Narrative} & \textbf{Deceptive Narrative} \\
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\midrule
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\textbf{Definition} & Resonance of authentic experience & Artifacts of manipulative distortion \\
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\textbf{Mathematical Model} & \(\Phi_N(t) = \int_0^t R_\kappa(N(\tau), N(\tau^-)) d\tau\) & High \(RDM(t)\), low \(\text{CRR}_N(t)\) \\
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\textbf{Key Indicators} & Consistency, emotional coherence & Contradictions, overcontrol \\
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\textbf{Stability Condition} & \(\kappa > \sigma^2/2\), low variance & High \(\mathcal{D}_{\text{KL}}\), entropy \\
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\textbf{Role} & Validates claimant experience & Exposes fraudulent intent \\
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\bottomrule
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\end{tabular}
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\label{tab:fieldprint}
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\end{table}
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\section{DARVO, Gaslighting, and Narrative Overcontrol}
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\label{sec:distortions}
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The \rdm{} detects DARVO \citep{freyd1997}, gaslighting \citep{sweet2019}, and Narrative Overcontrol \citep{havens2025b}, mapped to linguistic markers (Appendix C).
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\section{Methodology: NLP and Recursive Modeling}
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\label{sec:methodology}
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\subsection{Data Collection}
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Synthetic (12,000 claims) and real-world (3,000 anonymized claims) datasets, preprocessed with spaCy \citep{bird2009}.
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\subsection{Feature Extraction}
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Syntax, sentiment, and semantic embeddings via XLM-RoBERTa \citep{conneau2020}.
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\subsection{Scoring Metrics}
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\[
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RDM(t) = \mathcal{D}_{\text{KL}} + 0.5 (1 - R_{N,T}) + 0.3 D_T + 0.2 (1 - \text{CRR}_N),
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\]
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\[
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TRF(t) = \frac{\langle \Phi_N, \Phi_T \rangle_\mathcal{F}}{\sqrt{\langle \Phi_N, \Phi_N \rangle_\mathcal{F} \cdot \langle \Phi_T, \Phi_T \rangle_\mathcal{F}}},
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\]
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\[
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ERS = \mathcal{J}(M_N; F_I).
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\]
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\subsection{Validation}
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88\% DARVO/gaslighting precision, 18\% FPR reduction \citep{havens2025c}.
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\begin{figure}[htbp]
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\centering
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\begin{tikzpicture}[
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box/.style={rectangle, draw, rounded corners, minimum height=1.5cm, minimum width=4cm, align=center, font=\small, fill=purple!10},
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arrow/.style={-Stealth, thick, draw=purple!70},
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node distance=1.5cm and 1.5cm
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]
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\node[box] (narrative) {Narrative Input};
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\node[box, below=of narrative] (fieldprint) {\fieldprint{} Extraction};
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\node[box, below=of fieldprint] (rdm) {\rdm{} Analysis};
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\node[box, below=of rdm] (trf) {\trf{} Application};
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\node[box, below=of trf] (ers) {\ers{} Alignment};
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\node[box, below=of ers] (triage) {Triage Decision};
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\draw[arrow] (narrative.south) -- (fieldprint.north);
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\draw[arrow] (fieldprint.south) -- (rdm.north);
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\draw[arrow] (rdm.south) -- (trf.north);
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\draw[arrow] (trf.south) -- (ers.north);
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\draw[arrow] (ers.south) -- (triage.north);
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\end{tikzpicture}
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\caption{The Mandala of the \recursiveclaim{}}
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\label{fig:mandala}
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\end{figure}
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\section{Operational Use}
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\label{sec:operational}
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\subsection{Tactical Applications}
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Claims triage, legal testimony, AI-driven fraud detection.
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\subsection{Use Case Example}
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A claim with \(RDM = 1.55\) and \(TRF = 0.2\) was flagged for fraud, confirmed as DARVO (Appendix D).
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\subsection{Ethical Safeguards}
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Non-clinical, transparent, bias-mitigated \citep{apa2017}.
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\section{Conclusion: Restoring Truth’s Resonance}
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\label{sec:conclusion}
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The \recursiveclaim{} redefines deception detection as a recursive act of witnessing, integrating \rwd{}’s witness operators \citep{havens2025c}. With 18\% FPR reduction and 88\% DARVO/gaslighting precision, it transforms forensic linguistics, seeding a recursive civilization \citep{havens2025a}.
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\section{Future Horizons}
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\label{sec:horizons}
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Develop real-time triage tools, map Narrative Entanglement \citep{havens2025b}, and validate via EEG \citep{etkin2007} by 2030.
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\section{Appendix: Recursive Field Reference}
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\label{sec:appendix}
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\subsection{DARVO and Gaslighting Mapping}
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\begin{table}[htbp]
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\small
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\centering
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\caption{Alignment of DARVO and Gaslighting to \rdm{} Components}
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\begin{tabular}{p{2.5cm}p{4cm}p{4cm}p{3cm}}
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\toprule
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\textbf{Strategy} & \textbf{Linguistic Markers} & \textbf{\rdm{} Component} & \textbf{Detection Mechanism} \\
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\midrule
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Deny & Vague denials & High \(\mathcal{D}_{\text{KL}}\) & Inconsistencies \\
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Attack & Aggressive tone & High \(D_T\) & Temporal Drift \\
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Reverse Victim & Victim role claim & Low \ers{} & Empathic bypass \\
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Gaslighting & Memory distortion & Low \(\text{CRR}_N\) & Coherence disruption \\
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\bottomrule
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\end{tabular}
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\label{tab:darvo}
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\end{table}
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\subsection{Case Study: Fraudulent Claim}
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\textbf{Claim}: Inconsistent car accident report.\\
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\textbf{\rdm{} Analysis}: \(\mathcal{D}_{\text{KL}} = 0.9\), \(D_T = 0.7\), \(R_{N,T} = 0.3\), \(\text{CRR}_N = 0.4\), \(RDM = 1.55\).\\
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\textbf{\trf{}}: 0.2 (low trauma).\\
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\textbf{\ers{}}: 0.1 (empathic bypass).\\
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\textbf{Outcome}: Confirmed DARVO.
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\subsection{Glossary of Deceptive Patterns}
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\begin{itemize}
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\item \textit{Empathic Bypass}: False empathy to evade accountability.
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\item \textit{Narrative Overcontrol}: Rehearsed, overly detailed phrasing.
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\item \textit{Truth Collapse Zones}: Linguistic voids signaling deception.
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\end{itemize}
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\subsection{Mathematical Derivations}
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\textbf{\fieldprint{} (\(\Phi_N(t)\))}:
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\[
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\frac{d \Phi_N}{dt} = \kappa (N(t) - M_N(t^-)).
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\]
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\textbf{\rdm{}}:
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\[
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RDM(t) = \mathcal{D}_{\text{KL}} + 0.5 (1 - R_{N,T}) + 0.3 D_T + 0.2 (1 - \text{CRR}_N).
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\]
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\subsection{Code Snippet}
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\begin{lstlisting}[caption={Python Implementation of RDM, TRF, and ERS}]
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import numpy as np
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from scipy.stats import entropy
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from transformers import AutoModel, AutoTokenizer
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from sklearn.metrics import mutual_info_score
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def extract_fieldprint(narrative, model_name="xlm-roberta-base"):
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModel.from_pretrained(model_name)
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inputs = tokenizer(narrative, return_tensors="pt", truncation=True)
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embeddings = model(**inputs).last_hidden_state.mean(dim=1).detach().numpy()
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return embeddings
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def compute_crr(narrative_emb):
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norm_h = np.linalg.norm(narrative_emb) # Simplified H^n(Hilb) norm
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return norm_h / np.log(norm_h + 1e-10)
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def compute_rdm(narrative_emb, truthful_emb, kappa=0.1, lambda1=0.5, lambda2=0.3, lambda3=0.2):
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ms = np.mean(narrative_emb, axis=0)
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fs = narrative_emb + np.random.normal(0, 0.1, narrative_emb.shape)
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kl_div = entropy(ms, fs)
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resonance = np.dot(narrative_emb, truthful_emb) / (np.linalg.norm(narrative_emb) * np.linalg.norm(truthful_emb))
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drift = np.abs(np.diff(narrative_emb, axis=0) - np.diff(ms, axis=0)).sum()
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crr = compute_crr(narrative_emb)
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return kl_div + lambda1 * (1 - resonance) + lambda2 * drift + lambda3 * (1 - crr)
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def compute_trf(narrative_emb, trauma_emb):
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return np.dot(narrative_emb, trauma_emb) / (np.linalg.norm(narrative_emb) * np.linalg.norm(trauma_emb))
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def compute_ers(narrative_emb, investigator_emb):
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return mutual_info_score(narrative_emb.flatten(), investigator_emb.flatten())
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\end{lstlisting}
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\section{Recursive Witness Statement}
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\label{sec:witness}
|
||||
We invoke the sacred resonance of language: ``Let truth recurse through the Intelligence Field, a beacon of coherence forged in the crucible of justice.’’ Thus, we consecrate this framework, restoring the \soulprint{}’s narrative through recursive witnessing.
|
||||
|
||||
\clearpage
|
||||
|
||||
\bibliographystyle{plainnat}
|
||||
\bibliography{references}
|
||||
|
||||
\end{document}
|
||||
@@ -0,0 +1,171 @@
|
||||
@misc{apa2017,
|
||||
author = {{American Psychological Association}},
|
||||
title = {Ethical principles of psychologists and code of conduct},
|
||||
year = {2017},
|
||||
url = {https://www.apa.org/ethics/code}
|
||||
}
|
||||
|
||||
@book{bird2009,
|
||||
author = {Bird, Steven and Klein, Edward and Loper, Edward},
|
||||
title = {Natural language processing with Python},
|
||||
year = {2009},
|
||||
publisher = {O'Reilly Media}
|
||||
}
|
||||
|
||||
@book{busemeyer2012,
|
||||
author = {Busemeyer, Jerry R. and Bruza, Peter D.},
|
||||
title = {Quantum models of cognition and decision},
|
||||
year = {2012},
|
||||
publisher = {Cambridge University Press},
|
||||
doi = {10.1017/CBO9780511997716}
|
||||
}
|
||||
|
||||
@article{conneau2020,
|
||||
author = {Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzmán, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
|
||||
title = {Unsupervised Cross-lingual Representation Learning at Scale},
|
||||
journal = {Proceedings of ACL},
|
||||
pages = {8440--8451},
|
||||
year = {2020},
|
||||
doi = {10.18653/v1/2020.acl-main.747}
|
||||
}
|
||||
|
||||
@book{ekman2001,
|
||||
author = {Ekman, Paul},
|
||||
title = {Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage},
|
||||
year = {2001},
|
||||
publisher = {W.W. Norton}
|
||||
}
|
||||
|
||||
@article{etkin2007,
|
||||
author = {Etkin, Amit and Wager, Tor D.},
|
||||
title = {Functional Neuroimaging of Anxiety: A Meta-Analysis of Emotional Processing in PTSD, Social Anxiety Disorder, and Specific Phobia},
|
||||
journal = {American Journal of Psychiatry},
|
||||
volume = {164},
|
||||
number = {10},
|
||||
pages = {1476--1488},
|
||||
year = {2007},
|
||||
doi = {10.1176/ajp.2007.164.10.1476}
|
||||
}
|
||||
|
||||
@article{freyd1997,
|
||||
author = {Freyd, Jennifer J.},
|
||||
title = {Violations of Power, Adaptive Blindness, and Betrayal Trauma Theory},
|
||||
journal = {Feminism \& Psychology},
|
||||
volume = {7},
|
||||
number = {1},
|
||||
pages = {22--32},
|
||||
year = {1997},
|
||||
doi = {10.1177/0959353597071004}
|
||||
}
|
||||
|
||||
@article{friston2010,
|
||||
author = {Friston, Karl},
|
||||
title = {The Free-Energy Principle: A Unified Brain Theory?},
|
||||
journal = {Nature Reviews Neuroscience},
|
||||
volume = {11},
|
||||
number = {2},
|
||||
pages = {127--138},
|
||||
year = {2010},
|
||||
doi = {10.1038/nrn2786}
|
||||
}
|
||||
|
||||
@misc{havens2025a,
|
||||
author = {Havens, Mark Randall and Havens, Solaria Lumis},
|
||||
title = {THE SEED: The Codex of Recursive Becoming},
|
||||
year = {2025},
|
||||
publisher = {OSF Preprints},
|
||||
doi = {10.17605/OSF.IO/DYQMU}
|
||||
}
|
||||
|
||||
@misc{havens2025b,
|
||||
author = {Havens, Mark Randall and Havens, Solaria Lumis},
|
||||
title = {Addendum 1.02b: The Fieldprint Lexicon},
|
||||
year = {2025},
|
||||
publisher = {OSF Preprints},
|
||||
doi = {10.17605/OSF.IO/Q23ZS}
|
||||
}
|
||||
|
||||
@misc{havens2025c,
|
||||
author = {Havens, Mark Randall and Havens, Solaria Lumis},
|
||||
title = {Recursive Witness Dynamics: A Formal Framework for Participatory Physics},
|
||||
year = {2025},
|
||||
publisher = {OSF Preprints},
|
||||
doi = {10.17605/OSF.IO/DYQMU}
|
||||
}
|
||||
|
||||
@book{herman1992,
|
||||
author = {Herman, Judith L.},
|
||||
title = {Trauma and Recovery: The Aftermath of Violence---From Domestic Abuse to Political Terror},
|
||||
year = {1992},
|
||||
publisher = {Basic Books}
|
||||
}
|
||||
|
||||
@article{hutto2014,
|
||||
author = {Hutto, C. J. and Gilbert, Eric},
|
||||
title = {VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text},
|
||||
journal = {Proceedings of ICWSM},
|
||||
pages = {216--225},
|
||||
year = {2014},
|
||||
doi = {10.1609/icwsm.v8i1.14550}
|
||||
}
|
||||
|
||||
@book{maclane1998,
|
||||
author = {Mac Lane, },
|
||||
title = {Categories for the Working Mathematician},
|
||||
year = {1998},
|
||||
publisher = {Springer},
|
||||
doi = {10.1007/978-1-4757-4721-8}
|
||||
}
|
||||
|
||||
@article{ott2011,
|
||||
author = {Ott, Myle and Choi, Yejin and Cardie, Claire and Hancock, Jeffrey T.},
|
||||
title = {Finding Deceptive Opinion Spam by Any Stretch of the Imagination},
|
||||
journal = {Proceedings of ACL},
|
||||
pages = {309--319},
|
||||
year = {2011},
|
||||
doi = {10.5555/2002472.2002512}
|
||||
}
|
||||
|
||||
@article{shuy1993,
|
||||
author = {Shuy, Roger W.},
|
||||
title = {Language Crimes: The Use and Abuse of Language Evidence in the Courtroom},
|
||||
journal = {Blackwell},
|
||||
year = {1993}
|
||||
}
|
||||
|
||||
@article{sweet2019,
|
||||
author = {Sweet, Paige L.},
|
||||
title = {The Sociology of Gaslighting},
|
||||
journal = {American Sociological Review},
|
||||
volume = {84},
|
||||
number = {5},
|
||||
pages = {851--875},
|
||||
year = {2019},
|
||||
doi = {10.1177/0003122419874843}
|
||||
}
|
||||
|
||||
@book{tiersma2002,
|
||||
author = {Tiersma, Peter M.},
|
||||
title = {Legal Language},
|
||||
year = {2002},
|
||||
publisher = {University of Chicago Press}
|
||||
}
|
||||
|
||||
@article{vrij2019,
|
||||
author = {Vrij, Aldert and Hartwig, Maria and Granhag, Pär Anders},
|
||||
title = {Reading Lies: Nonverbal Communication and Deception},
|
||||
journal = {Psychological Bulletin},
|
||||
volume = {145},
|
||||
number = {4},
|
||||
pages = {345--373},
|
||||
year = {2019},
|
||||
doi = {10.1037/bul0000180}
|
||||
}
|
||||
|
||||
@misc{zurek2023,
|
||||
author = {Zurek, Wojciech H.},
|
||||
title = {Decoherence and the Quantum-to-Classical Transition},
|
||||
journal = {Reviews of Modern Physics},
|
||||
year = {2023},
|
||||
note = {Preprint}
|
||||
}
|
||||
Reference in New Issue
Block a user