Discoverability matters: Open access models and the translation of science into patents
Abdelghani Maddi (GEMASS), Chongjun Xi (GEMASS), Xiaoting Chen (GEMASS), Isabelle Dorsch (GEMASS), Marc-Andr\'e Simard (UdeM, CIRST)

TL;DR
This study investigates how open access publishing models influence the citation patterns and cognitive relevance of scientific literature in patents, revealing that visibility affects citation choices but not necessarily technological alignment.
Contribution
It provides a large-scale analysis linking open access publishing types to patent citations and semantic similarity, highlighting the role of publishing models in innovation.
Findings
Hybrid and bronze OA publications are disproportionately cited in patents.
Fully OA publications show higher semantic similarity to patent content.
Visibility and infrastructure influence the use of scientific knowledge in patents.
Abstract
Scientific research is a key input into technological innovation, yet not all scientific knowledge is equally mobilized in patents. This paper examines how different scientific publishing models shape both the selection of scientific publications cited in patents and their cognitive alignment with patented technologies. Using large-scale data on non-patent references linking patents to scientific publications, combined with metadata from OpenAlex, we compare the Open Access (OA) structure of patent-cited science to that of the scientific literature. We then assess cognitive alignment using semantic similarity between patent abstracts and the abstracts of cited publications, distinguishing between citations appearing in the front section of patents and those embedded in the body of patent texts. We find that patent citations disproportionately draw on publications disseminated through…
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