Samantha Dies

Samantha Dies, Courtney Maynard, Germans Savcisens et al.

Large language models (LLMs) are increasingly used as information sources, yet small changes in semantic framing can destabilize their truth judgments. We propose P-StaT (Perturbation Stability of Truth), an evaluation framework for testing belief stability under controlled semantic perturbations in representational and behavioral settings via probing and zero-shot prompting. Across sixteen open-source LLMs and three domains, we compare perturbations involving epistemically familiar Neither statements drawn from well-known fictional contexts (Fictional) to those involving unfamiliar Neither statements not seen in training data (Synthetic). We find a consistent stability hierarchy: Synthetic content aligns closely with factual representations and induces the largest retractions of previously held beliefs, producing up to $32.7\%$ retractions in representational evaluations and up to $36.3\%$ in behavioral evaluations. By contrast, Fictional content is more representationally distinct and comparatively stable. Together, these results suggest that epistemic familiarity is a robust signal across instantiations of belief stability under semantic reframing, complementing accuracy-based factuality evaluation with a notion of epistemic robustness.

Samantha Dies, David Liu, Tina Eliassi-Rad

Faculty hiring shapes the flow of ideas, resources, and opportunities in academia, influencing not only individual career trajectories but also broader patterns of institutional prestige and scientific progress. While traditional studies have found strong correlations between faculty hiring and attributes such as doctoral department prestige and publication record, they rarely assess whether these associations generalize to individual hiring outcomes, particularly for future candidates outside the original sample. Here, we consider faculty placement as an individual-level prediction task. Our data consist of temporal co-authorship networks with conventional attributes such as doctoral department prestige and bibliometric features. We observe that using the co-authorship network significantly improves predictive accuracy by up to 10% over traditional indicators alone, with the largest gains observed for placements at the most elite (top-10) departments. Our results underscore the role that social networks, professional endorsements, and implicit advocacy play in faculty hiring beyond traditional measures of scholarly productivity and institutional prestige. By introducing a predictive framing of faculty placement and establishing the benefit of considering co-authorship networks, this work provides a new lens for understanding structural biases in academia that could inform targeted interventions aimed at increasing transparency, fairness, and equity in academic hiring practices.