academic career advisors

Abstract: Many academic disciplines (in particular, mathematics and the theoretical sciences) face difficult questions about how to adapt their practices to the rise of highly capable artificial intelligence (AI). Before AI, a well-written research paper with a strong theoretical result acted as a reliable signal that its authors understood their field in depth. Now that AI is widely accessible, such signals have become cheaper to produce. As a result, they no longer reliably sort academics by their level of understanding. This has direct consequences for academic hiring. The age of AI raises a question for each side of the academic job market. Should an early-career researcher invest in a deep understanding of their field, or instead use AI to build a longer resume with many papers? Should a recruiter favor applicants with fewer papers who pass an interview aimed at probing understanding, or insist on both publication volume and a pass? In this paper, we develop a game-theoretic model of this two-sided dilemma. We classify all pure-strategy equilibria that can arise in this game and use learning simulations to identify the behaviors that populations of applicants and recruiters may learn to adopt. Our analysis provides a framework for reasoning about how academic hiring can adapt to AI and for evaluating practices that preserve the value of deep understanding.

Abstract: Mentorship provides mentees with knowledge, collaborators, and reputation, but when do these resources support scholarship beyond training? Using 239,931 bioscience mentor-mentee pairs from the Academic Family Tree (1800-2020) linked to OpenAlex metadata, we examine this question through mentees' publication in citation-elite journals, identified by three source-level citation indicators. We distinguish training-period mentor and peer resources from post-graduation topic and coauthorship network continuity. Analyses combine trajectory comparisons, three propensity-score matching designs, PPML, two-part model and non-overlapping landmarks relating continuity in post-graduation years 1-3 to publication outcomes in years 4-10 and 6-20. Citation-elite journal publishing during mentorship is positively associated with later publishing, but correlates differ by career stage. Close mentor collaboration and larger peer group size characterize training-period publishing, whereas both are negatively associated with post-graduation publication probability and intensity. Early topic and network continuity are positively associated with later output in landmark analyses; across extended conventional windows, topic continuity becomes less favorable, consistent with later agenda broadening. Network continuity shows the most stable positive association with publication intensity. The findings suggest a stage-dependent resource-conversion process: training resources may facilitate entry into citation-elite journal publishing, whereas sustained output is associated with how intellectual and relational ties are retained or reconfigured.