Selected Papers

Do "bad" citations have "good" effects?

Honglin Bao and Misha Teplistkiy

Early Preprint (Forthcoming, Nature Communications)

Twitter thread; Code and data

Presented at Organizational Modeling Society PhD Brown Bags 2023; HBS D^3 research workshop 2022 Slides

TL; DR: We use theoretical models to construct a counterfactual world with substantive citing only (e.g., learning from references), and we compare counterfactual vs. realistic worlds (with rhetorical citing -- citing without being inspired) to show that, although discouraged, rhetorical citing benefits academic community health and makes novel ideas more easily diffuse. The explanation for the effect is that the quality of creative products, like papers, is hard to discern and people thus use heuristics to judge them. In a world with substantive citing only, citations and attention would be concentrated among the highest-status papers, and that concentration would increase via the feedback loop of "the-rich-getting-richer". However, rhetorical citing weakens the social-reinforcement loop by redistributing focus from the few elite-quality papers to those "middle-quality" ones that are rhetorically useful in persuading readers (like supporting the citers' own claims). Mid-quality pieces benefit more because low-quality works lack persuasiveness.

Cultural Ties in Knowledge Diffusion

with Xiaoqin Yan, Tom Leppard, and Andrew P. Davis

Invited for submission to Poetics: Journal of Empirical Research on Culture, the Media and the Arts; Draft upon request

Presented at AS(ociology)A 2023 Science Knowledge and Technology section (by coauthor); HBS D^3 research workshop 2023 Slides

TL; DR: People study "social ties" in knowledge diffusion, for instance, citation, faculty placement, and collaboration. "Cultural tie" is a missing dimension, e.g., ties motivated by similar tastes -- innovation happens at both levels of social and symbolic (cultural) interactions. We construct a unique pairwise dataset including 6,441 school pairs across 114 sociology doctoral-granting institutions in the US, detailing their dyadic relationships (e.g., geographical co-residence). We infer each school pair’s cultural proximity with their dissertations. Using recent NLP advancements, we pinpoint "gatekeeping terminologies" that set apart mutually connected schools in the cultural-similarity network, suggesting that they occupy the same cultural niche formed by unique co-used cultural symbols. We discern key determinants that shape cultural convergence (e.g., matching status) and distinction (e.g., different-state public schools) using dyadic-cluster-robust inference with school-fixed effects.