The frequency of "brilliant" and "genius" in teaching evaluations predicts the representation of women and African Americans across fields

Women and African Americans-groups targeted by negative stereotypes about their intellectual abilities-may be underrepresented in careers that prize brilliance and genius. A recent nationwide survey of academics provided initial support for this possibility. Fields whose practitioners believed that natural talent is crucial for success had fewer female and African American PhDs. The present study seeks to replicate this initial finding with a different, and arguably more naturalistic, measure of the extent to which brilliance and genius are prized within a field. Specifically, we measured field-by-field variability in the emphasis on these intellectual qualities by tallying-with the use of a recently released online tool-the frequency of the words "brilliant" and "genius" in over 14 million reviews on RateMyProfessors.com, a popular website where students can write anonymous evaluations of their instructors. This simple word count predicted both women's and African Americans' representation across the academic spectrum. That is, we found that fields in which the words "brilliant" and "genius" were used more frequently on RateMyProfessors.com also had fewer female and African American PhDs. Looking at an earlier stage in students' educational careers, we found that brilliance-focused fields also had fewer women and African Americans obtaining bachelor's degrees. These relationships held even when accounting for field-specific averages on standardized mathematics assessments, as well as several competing hypotheses concerning group differences in representation. The fact that this naturalistic measure of a field's focus on brilliance predicted the magnitude of its gender and race gaps speaks to the tight link between ability beliefs and diversity

What makes a role model motivating for young girls? The effects of the role model’s growth versus fixed mindsets about ability and interest

Successful women role models can be—but are not always—effective in increasing pursuit of science, technology, engineering, and mathematics (STEM) careers among girls. What makes a woman role model motivating for young girls? An experimental study (N = 205 girls aged 5–8 years; 42.0% girls of color) investigated the effects of a role model’s messages about her own ability and interest. The model portrayed her ability and interest as quantities that developed over time (a growth mindset) or that had always been present (a fixed mindset). The role model’s growth (vs. fixed) mindset messages about ability—but not interest—increased girls’ interest and self-efficacy in the scientist’s field, but these effects were observed only among girls of color (ds = 0.56 and 0.65 for interest and self-efficacy, respectively). The findings contribute to theory on role models and growth mindsets, and they also have implications for the design of effective role model interventions

Memory Errors Reveal a Bias to Spontaneously Generalize to Categories

Much evidence suggests that, from a young age, humans are able to generalize information learned about a subset of a category to the category itself. Here, we propose that—beyond simply being able to perform such generalizations—people are biased to generalize to categories, such that they routinely make spontaneous, implicit category generalizations from information that licenses such generalizations. To demonstrate the existence of this bias, we asked participants to perform a task in which category generalizations would distract from the main goal of the task, leading to a characteristic pattern of errors. Specifically, participants were asked to memorize two types of novel facts: quantified facts about sets of kind members (e.g., facts about all or many stups) and generic facts about entire kinds (e.g., facts about zorbs as a kind). Moreover, half of the facts concerned properties that are typically generalizable to an animal kind (e.g., eating fruits and vegetables), and half concerned properties that are typically more idiosyncratic (e.g., getting mud in their hair). We predicted that—because of the hypothesized bias—participants would spontaneously generalize the quantified facts to the corresponding kinds, and would do so more frequently for the facts about generalizable (rather than idiosyncratic) properties. In turn, these generalizations would lead to a higher rate of quantified‐to‐generic memory errors for the generalizable properties. The results of four experiments (N = 449) supported this prediction. Moreover, the same generalizable‐versus‐idiosyncratic difference in memory errors occurred even under cognitive load, which suggests that the hypothesized bias operates unnoticed in the background, requiring few cognitive resources. In sum, this evidence suggests the presence of a powerful bias to draw generalizations about kinds.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112247/1/cogs12189.pd

Do Lions Have Manes? For Children, Generics Are About Kinds Rather Than Quantities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90604/1/j.1467-8624.2011.01708.x.pd

People’s Beliefs About Pronouns Reflect Both the Language They Speak and Their Ideologies

Pronouns often convey information about a person’s social identity (e.g., gender). Consequently, pronouns have become a focal point in academic and public debates about whether pronouns should be changed to be more inclusive, such as for people whose identities do not fit current pronoun conventions (e.g., gender non-binary individuals). Here, we make an empirical contribution to these debates by investigating which social identities lay speakers think that pronouns should encode and why. Across four studies, participants were asked to evaluate different types of real and hypothetical pronouns, including binary gender pronouns, race pronouns, and identity-neutral pronouns. We sampled participants from two languages with different pronoun systems: English (N = 1,120) and Turkish (N = 260). English pronouns commonly denote binary gender (e.g., he for men), whereas Turkish pronouns are identity-neutral (e.g., o for anyone). Participants’ reasoning about pronouns reflected both a familiarity preference (i.e., participants preferred the pronoun type used in their language) and—critically—participants’ social ideologies. In both language contexts, participants’ ideological beliefs that social groups are inherently distinct (essentialism) and should be hierarchal (social dominance orientation) predicted relatively greater endorsement of binary gender pronouns and race pronouns. A preregistered experimental study with an English-speaking sample showed that the relationship between ideology and pronoun endorsement is causal: Ideologies shape attitudes toward pronouns. Together, the present research contributes to linguistic and psychological theories concerning how people reason about language and informs policy-relevant questions about whether and how to implement language changes for social purposes

The pipeline project:Pre-publication independent replications of a single laboratory's research pipeline

This crowdsourced project introduces a collaborative approach to improving the reproducibility of scientific research, in which findings are replicated in qualified independent laboratories before (rather than after) they are published. Our goal is to establish a non-adversarial replication process with highly informative final results. To illustrate the Pre-Publication Independent Replication (PPIR) approach, 25 research groups conducted replications of all ten moral judgment effects which the last author and his collaborators had "in the pipeline" as of August 2014. Six findings replicated according to all replication criteria, one finding replicated but with a significantly smaller effect size than the original, one finding replicated consistently in the original culture but not outside of it, and two findings failed to find support. In total, 40% of the original findings failed at least one major replication criterion. Potential ways to implement and incentivize pre-publication independent replication on a large scale are discussed. (C) 2015 The Authors. Published by Elsevier Inc.</p

Data from a pre-publication independent replication initiative examining ten moral judgement effects

We present the data from a crowdsourced project seeking to replicate findings in independent laboratories before (rather than after) they are published. In this Pre-Publication Independent Replication (PPIR) initiative, 25 research groups attempted to replicate 10 moral judgment effects from a single laboratory's research pipeline of unpublished findings. The 10 effects were investigated using online/lab surveys containing psychological manipulations (vignettes) followed by questionnaires. Results revealed a mix of reliable, unreliable, and culturally moderated findings. Unlike any previous replication project, this dataset includes the data from not only the replications but also from the original studies, creating a unique corpus that researchers can use to better understand reproducibility and irreproducibility in science

The pipeline project: Pre-publication independent replications of a single laboratory's research pipeline

This crowdsourced project introduces a collaborative approach to improving the reproducibility of scientific research, in which findings are replicated in qualified independent laboratories before (rather than after) they are published. Our goal is to establish a non-adversarial replication process with highly informative final results. To illustrate the Pre-Publication Independent Replication (PPIR) approach, 25 research groups conducted replications of all ten moral judgment effects which the last author and his collaborators had “in the pipeline” as of August 2014. Six findings replicated according to all replication criteria, one finding replicated but with a significantly smaller effect size than the original, one finding replicated consistently in the original culture but not outside of it, and two findings failed to find support. In total, 40% of the original findings failed at least one major replication criterion. Potential ways to implement and incentivize pre-publication independent replication on a large scale are discussed