The preprint administers 45 questionnaires to 50 LLMs and constructs a global component explaining 47.1% of variance across their first-factor scores. It interprets this as an axis between self-attributing inner experience and answering in behavioral terms. It also proposes pi, the ratio of across-model variance under neutral prompting to variance under a prompt requiring simulation of an average human; across 1,312 items, pi has a weak association with factor-loading changes (rho=-.215). The pattern is useful as an exploratory probe of response style and experiential self-attribution, not as a measure of phenomenal experience. The human prompt explicitly asks models to converge on an average person, mechanically reducing variance, and the purported validations reuse the same responses. The repository audit also finds material defects: the advertised primary data are absent; matrices with 2 and 4 cases are included despite a stated minimum of 5; silhouette analysis uses 100 items under a top-80 label; Ward linkage is combined with correlation distance; and the bootstrap drops repeated draws because it builds the sample through a dictionary. The intervals and cluster validation are therefore unsupported as described. Differences between model variants also do not causally identify post-training effects.
Research question
What latent dimension dominates the psychometric differences among LLMs when they respond to many human instruments, and can it be interpreted through item content and the change in variance between responding as a model and simulating an average human?