Publishing Test-Ready Hypotheses When Experiments Are Out of Reach

Many promising ideas never reach experimentation, especially where access to laboratories, collaborators, or funding is scarce. This article proposes an editorial pathway for researchers in low- and middle-income countries (LMICs) or with limited infrastructure to publish robust, “test-ready” hypotheses. I argue that journals should make room for this contribution, and that a “test-ready” hypothesis should include: a clearly specified problem, grounding in the state of the art, falsifiable predictions, a viable experimental strategy for third parties, ethical appraisal, open licensing, a novelty scan, and a “reasons to be wrong” section. I describe a pathway that combines preprints (for priority and feedback) with defensive publication. Generative AI tools, used transparently and responsibly, can be crucial equalizing instruments, helping isolated researchers structure ideas and explore analogies. To avoid overloading the the system, this modality requires clear editorial filters and rigorous peer review. Positioned upstream from formats such as Registered Reports, well-selected hypotheses can accelerate evidence generation, register precedence, and offer a credible path of contribution for resource-limited researchers.