Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).In a recent review of research on gendered performance disparities in undergraduate science, technology, engineering, and mathematics (STEM) courses, Eddy and Brownell (2016) describe a confused research landscape: Some courses favor men, some favor women, and some show little bias. Their review calls specifically for systematic measurement of performance gaps across an array of disciplines and institutions, all accounting for prior academic performance, in the hope that emergent patterns might inform our understanding of "the relative contributions of different factors to performance and/or persistence in STEM." In this study, we answer this call, analyzing data on more than a million student enrollments in hundreds of courses drawn from five research-intensive public universities in the Big Ten Academic Alliance.We find evidence of statistically significant, persistent gendered performance differences (GPDs) in some large, introductory courses, differences that are also materially significant. In particular, men earned relatively higher grades than women in biology, chemistry, physics, accounting, and economics lecture courses, even after accounting for the influence of some measures of prior academic achievements Significant gendered performance differences are signals of systemic inequity in higher education. Understanding of these inequities has been hampered by the local nature of prior studies; consistent measures of performance disparity across many disciplines and institutions have not been available. Here, we report the first wide-ranging, multi-institution measures of gendered performance difference, examining more than a million student enrollments in hundreds of courses at five universities. After controlling for factors that relate to academic performance using optimal matching, we identify patterns of gendered performance difference that are consistent across these universities. Biology, chemistry, physics, accounting, and economics lecture courses regularly exhibit gendered performance differences that are statistically and materially significant, whereas lab courses in the same subjects do not. These results reinforce the importance of broad investigation of performance disparities across higher education. They also help focus equity research on the structure and evaluative schemes of these lecture courses.
Summary 1.Many ecologically based wildlife-habitat models provide only limited explanations of the observed data because they do not take account of the way in which key factors driving distribution interact with local management. If models are to be credible tools for developing solutions for wildlife management, they need to integrate scientific knowledge with the wealth of knowledge held by those who manage these resources. 2. In this study, we develop a participatory approach to integrate local knowledge from deer managers with formal scientific understanding and ecological spatial data in a simple Geographic Information System (GIS) to predict red deer Cervus elaphus L. distribution in the uplands of Scotland. We evaluate the extent to which the predictions are improved by this process. 3. The initial GIS prediction matched both managers' experience of deer locations and the independently derived deer point count data in around 50% of all cases.4. An analysis of interviews with managers indicated that for red deer, shelter provided by habitat characteristics was more important than topographic shelter or the forage value of the habitat. Disturbance, slope and elevation were also important. Analysis of the underlying spatial characteristics of those areas preferred by deer, as defined by managers, indicated similar relative importance of these factors in driving deer distribution. 5. The model was modified to incorporate the managers' knowledge and new predictions were evaluated against existing deer distribution data. The match between point counts and areas predicted by the model as being highly suitable for deer increased from around 50% to around 80%. 6. Synthesis and applications. Our evaluations demonstrate the validity of using local knowledge which can substantially improve the predictions from simple spatial models of deer habitat suitability. Our approach enables knowledge from different sources and at different spatial scales to be combined to give realistic predictions of deer distribution at an appropriate scale. Such participatory approaches to wildlife-habitat model development have the potential to improve communication and consensus across ownership boundaries where different management objectives exist.
Collaborative management is a widely accepted means of resolving conflict amongst natural resource stakeholders. Power sharing is central to most conceptualizations of collaboration, but theoretical insights about power are only rarely used to interrogate collaborative processes. Agenda-setting theory was used to analyse cases of collaborative deer management in England, Scotland and Indiana (USA). Collaborative management agendas across scales and social contexts were found to be primarily set by contextual factors, particularly stakeholders drawing on specific cultures and policies, and predefining issues. These findings highlight significant gaps between the theory and practice of collaboration. If, in practice, substantial power has been wielded in advance, the capacity of subsequent collaborative processes to share power amongst stakeholders may be severely limited. To provide opportunities for differing cultural perspectives to be expressed and challenged, convenors of collaborative processes therefore need to be aware of and reflexive upon existing power relationships and structures.
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