Unresolvable Identifier

arXiv:2605.04926 · detector doi_compliance · cross_source · 2026-05-19 14:01:48.266604+00:00

Paper page Integrity report arXiv Try DOI

Evidence text

Liang, L. & Acuna, D. demographicx: A python package for estimating gender and ethnicity using deep learning transformers.Zenodo https://doi. org/10.5281/zenodo4898367(2021). AcknowledgementsWe thank Brian Uzzi, Tara Sowrirajan, and Ching Jin for helpful discussion. We thank Wenhui Chen and Tara Sowrirajan for initial assistance on the data cleaning and meshing process. This work is partially supported by City University of Hong Kong Startup Grant No. 961070. Author Contributions StatementB.Z., C.Z. and H.P. designed the research. B.Z. and H.P. analyzed the data and wrote the draft of the paper. B.Z., C.Z. and H.P. revised the paper. B.Z. produced the visualizations. H.P. compiled the dataset and supervised the project. Competing Interests StatementThe authors declare no competing interests. 30 Unintended Negative Impacts of Promotional Language in Patent Evaluation (Supplemental Materials) Bingkun Zhao, Chenwei Zhang, Hao Peng (Dated: May 7, 2026) I. SUPPLEMENT AR Y T ABLE TABLE S1. Logistic regression models for predicting the likelihood of a patent application being granted. Model 1 includes only the percentage of promotional words and fixed-effects controls. Model 2 adds content controls. Model 3 adds applicant and team characteristics. Coefficient estimates are reported withp-values shown in parentheses. Dependent V ariable: Patentability Model 1 Model 2 Model 3 % of Promotional W ords -29.880 (p<0.001) -21.401 (p<0.001) -20.645 (p<0.001) Num. of Inventors -0.003 (p<0.00

Evidence payload

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