Large language models increasingly shape how academic citations are produced, suggested, and normalized. This paper examines the redistribution of academic credit produced by autocomplete and citation recommendation systems. While citation metrics traditionally reflect author intent, the syntactic design of LLM suggestion interfaces introduces a new variable: authority-bearing syntax. Through a double-blind experimental design comparing writing sessions with suggestion disabled, neutral suggestion, and authority-framed suggestion, this study quantifies shifts in citation concentration, novelty, and legitimacy phrasing. Results show that completions containing legitimizing structures (“as established by,” “following the seminal work of”) significantly increase concentration and reduce source diversity. The paper defines three measurable deltas, ΔC (concentration), ΔN (novelty), and ΔA (authority syntax), and demonstrates how predictive phrasing can algorithmically reproduce canonical hierarchies. As a corrective, it proposes a Fair Citation Prompt specification and an editorial checklist to detect and mitigate credit capture through syntactic bias. The findings suggest that citation fairness must be treated not only as a bibliometric concern but as a structural property of text generation systems, requiring explicit governance at the level of language form.. DOI Primary archive: https://doi.org/10.5281/zenodo.17287506 Secondary archive: https://doi.org/10.6084/m9.figshare.30295582 SSRN: Pending assignment (ETA: Q3 2025) Files