Probing the Density in the Galactic Center Region: Wind-Blown Bubbles and High-Energy Proton Constraints

Recent observations of the Galactic center in high-energy gamma-rays (above 0.1TeV) have opened up new ways to study this region, from understanding the emission source of these high-energy photons to constraining the environment in which they are formed. We present a revised theoretical density model of the inner 5pc surrounding Sgr A* based on the fact that the underlying structure of this region is dominated by the winds from the Wolf-Rayet stars orbiting Sgr A*. An ideal probe and application of this density structure is this high energy gamma-ray emission. We assume a proton-scattering model for the production of these gamma-rays and then determine first whether such a model is consistent with the observations and second whether we can use these observations to further constrain the density distribution in the Galactic center.Comment: 36 pages including 17 figures, submitted to ApJ, comments welcom

Finding Common Ground: Synthesizing Divergent Theoretical Views to Promote Women's STEM Pursuits

A range of theories seek to explain gender gaps in STEM, and the literature on social and motivational factors implicated in women’s STEMs pursuits is burgeoning. We contend that a next-generation strategy is needed to translate multiple and complex theories to practice: We focus on the overlap of multiple social psychological theories to propose common-ground strategies to foster women’s interest and participation in STEM. Building upon the foundational work of lone-theory approaches is a nextgeneration intervention approach that identifies where different theories have common ground – that is, where a particular intervention strategy might capitalize on multiple psychological mechanisms to yield benefit. We focus in particular on theories relevant to two incongruities that contribute to the gender gap in STEM: the incongruity between women and STEM (discussed in theories about stereotyping/discrimination, social identity, and stereotype threat), and the incongruity between STEM and student values (discussed in theories about expectancy-value, goal congruity, and work-family conflict). Three core strategies encompass multiple mechanisms described from these scientific frameworks, and these form the basis for intervention tactics: 1) Challenge stereotypes; 2) Align STEM activities with students’ values; 3) Cultivate growth mindsets related to STEM ability. We outline opportunities for structural change within educational and occupational settings that can enhance and sustain interest

A Goal Congruity Model of Role Entry, Engagement, and Exit: Understanding Communal Goal Processes in STEM Gender Gaps

The goal congruity perspective provides a theoretical framework to understand how motivational processes influence and are influenced by social roles. In particular, we invoke this framework to understand communal goal processes as proximal motivators of decisions to engage in science, technology, engineering, and mathematics (STEM). STEM fields are not perceived as affording communal opportunities to work with or help others, and understanding these perceived goal affordances can inform knowledge about differences between (a) STEM and other career pathways and (b) women’s and men’s choices. We review the patterning of gender disparities in STEM that leads to a focus on communal goal congruity (Part I), provide evidence for the foundational logic of the perspective (Part II), and explore the implications for research and policy (Part III). Understanding and transmitting the opportunities for communal goal pursuit within STEM can reap widespread benefits for broadening and deepening participation

Many Labs 3: Evaluating Participant Pool Quality across the Academic Semester via Replication

The university participant pool is a key resource for behavioral research, and data quality is believed to vary over the course of the academic semester. This crowdsourced project examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools (N = 2696) and with an online sample (N = 737). Weak time of semester effects were observed on data quality indicators, participant sex, and a few individual differences—conscientiousness, mood, and stress. However, there was little evidence for time of semester qualifying experimental or correlational effects. The generality of this evidence is unknown because only a subset of the tested effects demonstrated evidence for the original result in the whole sample. Mean characteristics of pool samples change slightly during the semester, but these data suggest that those changes are mostly irrelevant for detecting effects

Many Labs 3: Evaluating participant pool quality across the academic semester via replication

The university participant pool is a key resource for behavioral research, and data quality is believed to vary over the course of the academic semester. This crowdsourced project examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools (N = 2696) and with an online sample (N = 737). Weak time of semester effects were observed on data quality indicators, participant sex, and a few individual differences—conscientiousness, mood, and stress. However, there was little evidence for time of semester qualifying experimental or correlational effects. The generality of this evidence is unknown because only a subset of the tested effects demonstrated evidence for the original result in the whole sample. Mean characteristics of pool samples change slightly during the semester, but these data suggest that those changes are mostly irrelevant for detecting effects

Many Labs 3: Evaluating participant pool quality across the academic semester via replication

Many Labs 3 is a crowdsourced project that systematically evaluated time-of-semester effects across many participant pools. See the Wiki for a table of contents of files and to download the manuscript

Many Labs 3: Evaluating participant pool quality across the academic semester via replication

The university participant pool is a key resource for behavioral research, and data quality is believed to vary over the course of the academic semester. This crowdsourced project examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools (N = 2696) and with an online sample (N = 737). Weak time of semester effects were observed on data quality indicators, participant sex, and a few individual differences conscientiousness, mood, and stress. However, there was little evidence for time of semester qualifying experimental or correlational effects. The generality of this evidence is unknown because only a subset of the tested effects demonstrated evidence for the original result in the whole sample. Mean characteristics of pool samples change slightly during the semester, but these data suggest that those changes are mostly irrelevant for detecting effects. (C) 2015 Elsevier Inc. All rights reserved

Many Labs 3: Evaluating participant pool quality across the academic semester via replication

The university participant pool is a key resource for behavioral research, and data quality is believed to vary over the course of the academic semester. This crowdsourced project examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools (N = 2696) and with an online sample (N = 737). Weak time of semester effects were observed on data quality indicators, participant sex, and a few individual differences—conscientiousness, mood, and stress. However, there was little evidence for time of semester qualifying experimental or correlational effects. The generality of this evidence is unknown because only a subset of the tested effects demonstrated evidence for the original result in the whole sample. Mean characteristics of pool samples change slightly during the semester, but these data suggest that those changes are mostly irrelevant for detecting effects

Many Labs 3: Evaluating participant pool quality across the academic semester via replication

The university participant pool is a key resource for behavioral research, and data quality is believed to vary over the course of the academic semester. This crowdsourced project examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools (N = 2696) and with an online sample (N = 737). Weak time of semester effects were observed on data quality indicators, participant sex, and a few individual differences conscientiousness, mood, and stress. However, there was little evidence for time of semester qualifying experimental or correlational effects. The generality of this evidence is unknown because only a subset of the tested effects demonstrated evidence for the original result in the whole sample. Mean characteristics of pool samples change slightly during the semester, but these data suggest that those changes are mostly irrelevant for detecting effects. (C) 2015 Elsevier Inc. All rights reserved