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  • Item type:Publication,
    A Protocol to Assess Contextual Factors During Program Impact Evaluation: A Case Study of a STEM Gender Equity Intervention in Higher Education Open PDF 
    (2023)
    Nobrega, S
    ;
    Edwards, K
    ;
    El Ghaziri, M
    ;
    Giacobbe, L
    ;
    Rice, S
    Program evaluations that lack experimental design often fail to produce evidence of impact because there is no available control group. Theory-based evaluations can generate evidence of a program's causal effects if evaluators collect evidence along the theorized causal chain and identify possible competing causes. However, few methods are available for assessing competing causes in the program environment. Effect Modifier Assessment (EMA) is a method previously used in smaller-scale studies to assess possible competing causes of observed changes following an intervention. In our case study of a university gender equity intervention, EMA generated useful evidence of competing causes to augment program evaluation. Top-down administrative culture, poor experiences with hiring and promotion, and workload were identified as impeding forces that might have reduced program benefits. The EMA addresses a methodological gap in theory-based evaluation and might be useful in a variety of program settings.
  • Item type:Publication,
    Predicting the future impact of Computer Science researchers: Is there a gender bias? Open PDF 
    (2022)
    Kuppler, M.
    The advent of large-scale bibliographic databases and powerful prediction algorithms led to calls for data-driven approaches for targeting scarce funds at researchers with high predicted future scientific impact. The potential side-effects and fairness implications of such approaches are unknown, however. Using a large-scale bibliographic data set of N = 111,156 Computer Science researchers active from 1993 to 2016, I build and evaluate a realistic scientific impact prediction model. Given the persistent under-representation of women in Computer Science, the model is audited for disparate impact based on gender. Random forests and Gradient Boosting Machines are used to predict researchers’ h-index in 2010 from their bibliographic profiles in 2005. Based on model predictions, it is determined whether the researcher will become a high-performer with an h-index in the top-25% of the discipline-specific h-index distribution. The models predict the future h-index with an accuracy of R2= 0.875 and correctly classify 91.0% of researchers as high-performers and low-performers. Overall accuracy does not vary strongly across researcher gender. Nevertheless, there is indication of disparate impact against women. The models under-estimate the true h-index of female researchers more strongly than the h-index of male researchers. Further, women are 8.6% less likely to be predicted to become high-performers than men. In practice, hiring, tenure, and funding decisions that are based on model predictions risk to perpetuate the under-representation of women in Computer Science. © 2022, The Author(s).
  • Item type:Publication,
    Gender gaps in scientific performance: a longitudinal matching study of health sciences researchers Open PDF 
    (2020)
    Frandsen, T.F.
    ;
    Jacobsen, R.H.
    ;
    Ousager, J.
    The existence of gender disparities in academia is well documented. Many explanations have been proposed and productivity is one of the most used variables to explain a possible correlation between gender and differences in academic rank or leadership positions. The literature on the existence of a productivity gender gap is inconclusive which may due to the variety of study designs. This article presents the results of a longitudinal bibliometric study of health science researchers controlling for sub-disciplinary affiliation, education, year of enrollment and age. The productivity and impact of the researchers are analyzed during a 16-year period. We find no or little difference in productivity or impact among the group of health sciences researchers from the time of enrollment in the Ph.D. program and 10 years beyond, and women outperform men in some cases. There are negligible differences in productivity and impact prior to enrollment. The implications of the findings are discussed. © 2020, Akadémiai Kiadó, Budapest, Hungary.
  • Item type:Publication,
    Overcoming the gender bias in ecology and evolution: is the double-anonymized peer review an effective pathway over time? Open PDF 
    (2023)
    Cassia-Silva, C
    ;
    Rocha, BS
    ;
    Lievano-Latorre, LF
    ;
    Sobreiro, MB
    ;
    Diele-Viegas, LM
    Male researchers dominate scientific production in science, technology, engineering, and mathematics (STEM). However, potential mechanisms to avoid this gender imbalance remain poorly explored in STEM, including ecology and evolution areas. In the last decades, changes in the peer-review process towards double-anonymized (DA) have increased among ecology and evolution (EcoEvo) journals. Using comprehensive data on articles from 18 selected EcoEvo journals with an impact factor >1, we tested the effect of the DA peer-review process in female-leading (i.e., first and senior authors) articles. We tested whether the representation of female-leading authors differs between double and single-anonymized (SA) peer-reviewed journals. Also, we tested if the adoption of the DA by previous SA journals has increased the representativeness of female-leading authors over time. We found that publications led by female authors did not differ between DA and SA journals. Moreover, female-leading articles did not increase after changes from SA to DA peer-review. Tackling female underrepresentation in science is a complex task requiring many interventions. Still, our results highlight that adopting the DA peer-review system alone could be insufficient in fostering gender equality in EcoEvo scientific publications. Ecologists and evolutionists understand how diversity is important to ecosystems' resilience in facing environmental changes. The question remaining is: why is it so difficult to promote and keep this "diversity" in addition to equity and inclusion in the academic environment? We thus argue that all scientists, mentors, and research centers must be engaged in promoting solutions to gender bias by fostering diversity, inclusion, and affirmative measures.