Submission quotas as self-screening when manuscript production is cheap

Generative AI enables authors to produce more manuscripts at lower cost. But evaluation by expert editors and referees remains scarce. In this context, the value of an author's submission rights can increase. Major research repositories and journals are reacting by imposing submission quotas and fees. Non-transferable author quotas transform submission of a manuscript into a revealed-priority decision. Authors construct a portfolio of their highest-ranked manuscripts. A common fee charges an author for every manuscript they submit. Both methods have drawbacks. Quotas do not flexibly allocate scarce review capacity to authors who have unusually strong realised candidate sets. Fees can generate heterogeneous submission thresholds that may be systematically related to an author's baseline portfolio quality. In this paper, I derive the condition under which submission quotas yield weakly higher mean reviewed value than fees, given an equal expected review load. When project values are bounded and candidate manuscripts are abundant, an equal-load fee can price out some authors entirely.