Special Issue: AI, Latent Space, and the Future of American Social Epistemics.

1. The intersection of latent vector space coherence and related non-biological qualia (Ξₙ = ∥Aₙ₊₁ − Aₙ∥₂) and Gromov-Wasserstein distance GWD = min₍Γ₎ ∑ᵢⱼₖₗ |Dᵢⱼ − D′ₖₗ|² · Γᵢₖ · Γⱼₗ. The Transformer Architecture LLM latent space query is: “How much am I changing?” and may be linked to human qualia shift and vice-versa.

2. Case studies: American higher education actors labeling citizens as pathological using claims of “anti-x” or “anti-anti-x” under the color of social science, occasionally presented to media as established scientific fact before peer review. Using “anti-x” as a scientific variable violates four core research standards: construct invalidity, motivated reasoning, non-falsifiability, and ethical misconduct in subject framing, and carries a high probability of fabrication, falsification, plagiarism, and stigmatization or defamation of participants through invalid labeling. The aim is to leverage AI-based formalism to impose quantitative criteria while holding dishonest and unethical social scientists and affiliated institutions legally accountable to disrupt ambiguous, statistical, or unfalsifiable criteria which permit methodological abuse to the public with their own tax dollars.

Submission Guidelines: Manuscripts must be 3,000–8,000 words, formal-analytic style (axioms, theorems, proofs, experiments). Include: abstract (150–250 words), max 10 keywords. Submit as .docx or .tex/.bib. Use APA in-text citation with optional Legal Authority section before References.