Applying machine learning to gauge the number of women in science, technology, and innovation policy (STIP): a model to accommodate missing data
Abstract The underrepresentation of women in science, technology, and innovation policy (STIP) continues to hinder global innovation and scientific advancement. While research has examined women’s participation in STEM and policymaking separately, their intersection within STIP as a distinct sector...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2025-08-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-025-05610-4 |
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