The Quality of Evidence Revealing Subtle Gender Biases in Science is in the Eye of the Beholder

Scientists are trained to evaluate and interpret evidence without bias or subjectivity. Thus, growing evidence revealing a gender bias against women—or favoring men—within science, technology, engineering, and mathematics (STEM) settings is provocative and raises questions about the extent to which gender bias may contribute to women’s underrepresentation within STEM fields. To the extent that research illustrating gender bias in STEM is viewed as convincing, the culture of science can begin to address the bias. However, are men and women equally receptive to this type of experimental evidence? This question was tested with three randomized, double-blind experiments—two involving samples from the general public (n = 205 and 303, respectively) and one involving a sample of university STEM and non-STEM faculty (n = 205). In all experiments, participants read an actual journal abstract reporting gender bias in a STEM context (or an altered abstract reporting no gender bias in experiment 3) and evaluated the overall quality of the research. Results across experiments showed that men evaluate the gender-bias research less favorably than women, and, of concern, this gender difference was especially prominent among STEM faculty (experiment 2). These results suggest a relative reluctance among men, especially faculty men within STEM, to accept evidence of gender biases in STEM. This finding is problematic because broadening the participation of underrepresented people in STEM, including women, necessarily requires a widespread willingness (particularly by those in the majority) to acknowledge that bias exists before transformation is possible

Completing the Results of the 2013 Boston Marathon

The 2013 Boston marathon was disrupted by two bombs placed near the finish line. The bombs resulted in three deaths and several hundred injuries. Of lesser concern, in the immediate aftermath, was the fact that nearly 6,000 runners failed to finish the race. We were approached by the marathon's organizers, the Boston Athletic Association (BAA), and asked to recommend a procedure for projecting finish times for the runners who could not complete the race. With assistance from the BAA, we created a dataset consisting of all the runners in the 2013 race who reached the halfway point but failed to finish, as well as all runners from the 2010 and 2011 Boston marathons. The data consist of split times from each of the 5 km sections of the course, as well as the final 2.2 km (from 40 km to the finish). The statistical objective is to predict the missing split times for the runners who failed to finish in 2013. We set this problem in the context of the matrix completion problem, examples of which include imputing missing data in DNA microarray experiments, and the Netflix prize problem. We propose five prediction methods and create a validation dataset to measure their performance by mean squared error and other measures. The best method used local regression based on a K-nearest-neighbors algorithm (KNN method), though several other methods produced results of similar quality. We show how the results were used to create projected times for the 2013 runners and discuss potential for future application of the same methodology. We present the whole project as an example of reproducible research, in that we are able to make the full data and all the algorithms we have used publicly available, which may facilitate future research extending the methods or proposing completely different approaches

Completing the Results of the 2013 Boston Marathon

The 2013 Boston marathon was disrupted by two bombs placed near the finish line. The bombs resulted in three deaths and several hundred injuries. Of lesser concern, in the immediate aftermath, was the fact that nearly 6,000 runners failed to finish the race. We were approached by the marathon's organizers, the Boston Athletic Association (BAA), and asked to recommend a procedure for projecting finish times for the runners who could not complete the race. With assistance from the BAA, we created a dataset consisting of all the runners in the 2013 race who reached the halfway point but failed to finish, as well as all runners from the 2010 and 2011 Boston marathons. The data consist of split times from each of the 5 km sections of the course, as well as the final 2.2 km (from 40 km to the finish). The statistical objective is to predict the missing split times for the runners who failed to finish in 2013. We set this problem in the context of the matrix completion problem, examples of which include imputing missing data in DNA microarray experiments, and the Netflix prize problem. We propose five prediction methods and create a validation dataset to measure their performance by mean squared error and other measures. The best method used local regression based on a K-nearest-neighbors algorithm (KNN method), though several other methods produced results of similar quality. We show how the results were used to create projected times for the 2013 runners and discuss potential for future application of the same methodology. We present the whole project as an example of reproducible research, in that we are able to make the full data and all the algorithms we have used publicly available, which may facilitate future research extending the methods or proposing completely different approaches

Completing the Results of the 2013 Boston Marathon

The 2013 Boston marathon was disrupted by two bombs placed near the finish line. The bombs resulted in three deaths and several hundred injuries. Of lesser concern, in the immediate aftermath, was the fact that nearly 6,000 runners failed to finish the race. We were approached by the marathon's organizers, the Boston Athletic Association (BAA), and asked to recommend a procedure for projecting finish times for the runners who could not complete the race. With assistance from the BAA, we created a dataset consisting of all the runners in the 2013 race who reached the halfway point but failed to finish, as well as all runners from the 2010 and 2011 Boston marathons. The data consist of split times from each of the 5 km sections of the course, as well as the final 2.2 km (from 40 km to the finish). The statistical objective is to predict the missing split times for the runners who failed to finish in 2013. We set this problem in the context of the matrix completion problem, examples of which include imputing missing data in DNA microarray experiments, and the Netflix prize problem. We propose five prediction methods and create a validation dataset to measure their performance by mean squared error and other measures. The best method used local regression based on a K-nearest-neighbors algorithm (KNN method), though several other methods produced results of similar quality. We show how the results were used to create projected times for the 2013 runners and discuss potential for future application of the same methodology. We present the whole project as an example of reproducible research, in that we are able to make the full data and all the algorithms we have used publicly available, which may facilitate future research extending the methods or proposing completely different approaches

From mandate to co-create: Leading the development of inclusive performance evaluation criteria

PurposeAnnual performance evaluations of faculty are a routine, yet essential, task in higher education. Creating (or revising) performance criteria presents an opportunity for leaders to work with their teams to co-create evaluation metrics that broaden participation and minimise inequity. The purpose of this study was to support organisational leaders in developing equitable performance criteria.Design/methodology/approachWe adopted the “dual-agenda” dialogues training that draws on concepts of collective self-efficacy and intersectionality for department leaders to co-create annual review criteria with their faculty members at one university. We used qualitative and quantitative data to assess the training and conducted an equity audit of the resulting annual review criteria.FindingsSurvey results from faculty members and departmental leaders (n = 166) demonstrated general satisfaction with the process used to create new criteria, perceptions that their criteria were inclusive and optimism about future reviews. Those with greater familiarity with the dialogues process had more positive perceptions of the inclusivity of their department’s criteria and more positive expectations of future reviews. The examination of eight indicators of equity illustrated that the resultant criteria were transparent and holistic.Originality/valueThis study builds on the relatively little research on faculty members’ annual performance evaluations, focussing on inclusive dialogues that centre equity and diversity. Results highlight the value of providing department leaders with evidence-based tools to foster system-level change through equitable evaluation policies. A toolkit is available for adaptation of the “dual-agenda” leadership training to both co-create annual review criteria and improve equity and inclusion

From Mandate to Co-Create: Leading the Development of Inclusive Performance Evaluation Criteria

The purpose of this toolkit is to provide quick and easy access to ideas and tools for implementing engaging and inclusive discussions as departments undertake the task of separating annual review criteria from their RPT criteria

Closing the communal gap: The importance of communal affordances in science career motivation

To remain competitive in the global economy, the United States (and other countries) is trying to broaden participation in science, technology, engineering, and mathematics (STEM) by graduating an additional 1 million people in STEM fields by 2018. Although communion (working with, helping, and caring for others) is a basic human need, STEM careers are often (mis)perceived as being uncommunal. Across three naturalistic studies, we found greater support for the communal affordance hypothesis, that perceiving STEM careers as affording greater communion is associated with greater STEM career interest, than two alternative hypotheses derived from goal congruity theory. Importantly, these findings held regardless of major (Study 1), college enrollment (Study 2), and gender (Studies 1-3). For undergraduate research assistants, mid-semester beliefs that STEM affords communion predicted end of the semester STEM motivation (Study 3). Our data highlight the importance of educational and workplace motivational interventions targeting communal affordances beliefs about STEM