Applying Machine Learning to Measure Women’s Representation in Science, Technology, and Innovation Policy (STIP): A Novel Model Addressing Missing Data
Published in Humanities and Social Sciences Communications – August 4, 2025
The persistent underrepresentation of women in science, technology, and innovation policy (STIP) continues to pose a barrier to global scientific progress and innovation. Recognizing a significant gap, a new study published in Humanities and Social Sciences Communications introduces a groundbreaking machine learning framework designed to quantify and forecast the presence of women in STIP roles, notably addressing challenges posed by missing national data.
The Challenge of Assessing Women’s Participation in STIP
STIP plays a critical role in guiding a country’s scientific discovery, technological advancement, and workforce development. It impacts societal development, economic growth, and overall community health. However, women remain underrepresented within this influential sector despite evidence underscoring the positive impact of gender diversity on innovation and policymaking outcomes.
While previous research has separately explored women’s involvement in STEM fields and policymaking, the unique intersection found in STIP has been largely overlooked. Moreover, a lack of comprehensive domestic data on women’s representation within STIP has hampered the ability to conduct meaningful analyses and develop effective, targeted policies.
Dr. Caitlin Meyer, lead author of the study, emphasized: “Accurate measurement in this domain has been a significant challenge due to incomplete data from numerous countries, which limits understanding of the scale and nature of women’s participation in this critical area.”
Innovative Machine Learning Framework to Bridge Data Gaps
The research team analyzed data from 60 countries, employing a hybrid machine learning methodology that combined models including Linear Regression, ElasticNet, Lasso Regression, Ridge Regression, and Support Vector Regression (SVR). To effectively handle missing data, the team integrated advanced K-Nearest Neighbors (KNN) imputation techniques. Additionally, feature engineering utilized latent representations from autoencoders to enhance data representation and prediction accuracy.
Among the models tested, the Support Vector Regression model demonstrated the highest predictive performance, achieving an R² score of 0.835, along with favorable error metrics (mean absolute error of 0.2677 and root mean square error of 0.406). These metrics indicate a high level of precision in estimating women’s representation in the STIP sector.
Key Findings: Quotas vs. Outcomes and Disconnect Between STEM and Policy Leadership
One unexpected result emerged when evaluating the impact of formal gender quota systems. Countries without formal quota systems exhibited slightly better predictive performance in terms of women’s representation (median and mean values around 0.3675), compared to those with established quotas. This suggests that quotas alone may not sufficiently address structural barriers impacting gender equity in STIP.
Furthermore, the study revealed a weak correlation (r = 0.11) between women’s participation rates in STEM fields and their leadership roles in policymaking within STIP. This disconnect underscores that increasing women’s STEM participation does not automatically translate into proportional representation in high-level policy roles, highlighting the need for more holistic approaches.
Implications for Policy and Future Research
The researchers argue that these findings challenge simplistic assumptions about the effectiveness of quota policies and emphasize the necessity of addressing broader systemic challenges—such as cultural biases, institutional practices, and informal barriers—to achieve sustainable gender equity in STIP.
This novel machine learning framework not only offers the first reliable method for measuring women’s representation specifically within STIP but also provides a foundation for evidence-based policy formulation. By enabling more accurate measurement and predictive insights, the model equips policymakers and researchers with a powerful tool to evaluate interventions and design inclusive policies that foster meaningful participation of women in science and technology governance.
Closing the Data Gap for Better Outcomes
The study highlights the critical need to overcome the longstanding data deficiencies that have hindered comprehensive analysis of gender disparities in STIP. Without robust data collection and analytical tools, efforts to promote gender equity in this sector face significant challenges. The authors call for intensified efforts to gather comprehensive, high-quality data globally, complemented by interdisciplinary research approaches.
Dr. Du Baogui, a co-author, noted: “Addressing the data gap is essential not only for understanding women’s current participation but also for designing policies that effectively dismantle the systemic barriers female professionals face in STIP.”
Conclusion
By leveraging advanced machine learning techniques, this research marks a significant step forward in quantifying and understanding women’s role within the science, technology, and innovation policy arena. It provides a rigorous analytical framework that can help transform how gender equity is pursued in this critical sector, ultimately facilitating greater inclusion, innovation, and scientific progress worldwide.
References:
- UNESCO (2024). Women in Science Report.
- UN Women (2023). Women in Parliament: Global Statistics.
- Meyer, C., Baogui, D., & Gouda, M. A. (2025). Applying machine learning to gauge the number of women in science, technology, and innovation policy (STIP): a model to accommodate missing data. Humanities and Social Sciences Communications, 12, Article 1245. https://doi.org/10.1057/s41599-025-01245
For further information, readers are encouraged to access the full open-access article available through Humanities and Social Sciences Communications.
