8468 research outputs found
Sort by
Action shot of men\u27s basketball team member Kevin Cherry, circa late 1970s-early 1980s
No full textPhotograph of Clark University men\u27s basketball team member Kevin Cherry during a game, circa late 1970s to early 1980s.
All photographs in this collection were digitized between 2022 and 2023.
The photographs in this collection are part of the Photographs and Media record group of Clark University’s Archives & Special Collections.https://commons.clarku.edu/basketball/1045/thumbnail.jp
Captain Judy Hodge and Lynne Howes from women\u27s basketball team, 1984
No full textPhotograph of Captain Judy Hodge and Lynne Howes from the Clark University women\u27s basketball team, 1984.
All photographs in this collection were digitized between 2022 and 2023. The photographs in this collection are part of the Photographs and Media record group of Clark University’s Archives & Special Collections.https://commons.clarku.edu/basketball/1164/thumbnail.jp
Action shot of men\u27s basketball game [13], 1955
No full textAn action photograph of the Clark University men\u27s basketball team during a game, 1955.
All photographs in this collection were digitized between 2022 and 2023.
The photographs in this collection are part of the Photographs and Media record group of Clark University’s Archives & Special Collections.https://commons.clarku.edu/basketball/1069/thumbnail.jp
Exploring the Economic Geographies of Sustainability Transitions: Commentary and Agenda
No full textThis commentary provides a short review of the history and current state of transition studies, and explores how a deepened exchange with economic geographers could be fostered at the geography of sustainability transitions interface along three fronts. First, we argue that a combined transitions–economic geography perspective allows elucidating how the coevolution of technologies, institutions, and actor networks creates multiscalar and spatially uneven opportunity spaces for transformative change. Second, it provides a deeper process-based understanding of structural change trajectories, emphasizing the social construction of material and institutional elements and their alignment into socio-technical configurations that work in addressing wicked sustainability problems. Third, it creates novel inroads for conceptualizing—and critically questioning—normative, policy-related concerns around how to achieve more just, resilient, and environmentally sustainable futures. The article concludes with epistemological and strategic considerations on how to further advance geography of transitions perspectives. © 2025 Clark University
Different Beliefs About Collective Victimisation Predict Support for Distinct Resistance Strategies: Survey Findings Among the Kurdish Diaspora
No full textPeople make sense of their group\u27s collective victimisation in different ways. These different collective victimisation beliefs may be linked to distinct resistance strategies against the ingroup\u27s victimisation. However, most studies have only examined a few collective victimisation beliefs and resistance strategies, limited to prototypical collective action and failed to consider which constructs are most relevant for the given context. Addressing these gaps, the present study uses a multimethod approach with findings from a Q methodology study informing a survey study to examine the relationship between context-relevant, understudied collective victimisation beliefs and resistance strategies among Kurds from Turkey and Northern Kurdistan living in Germany (N = 281). Additionally, we explored the potentially differential predictive power of three distinct holistic viewpoints on collective victimisation (assessed through vignettes) compared to more commonly used, standard items measuring individual constructs. Our results show that collective victimisation beliefs are important predictors of different forms of resistance: while perceived restrictions of the ingroup\u27s collective cultural autonomy predicted engagement in both cultural resistance and nonviolent collective action, the perceived importance of maintaining the victimised nation\u27s strength and unity predicted support for cultural resistance and for violent resistance against oppressors. © 2025 The Author(s). Journal of Community & Applied Social Psychology published by John Wiley & Sons Ltd
Creating the 15-minute City: Public Engagement Strategies to Move People Away from Cars and Towards a More Walkable Worcester
No full textThe study of urban planning there has recently been a resurgence in the idea of a 15-minute city. A 15-minute city is a city where vital destinations are within a 15- minute walk or cycle from any resident. These cities flip the current car-centric method of transportation planning on its head, and instead ask planners to prioritize pedestrians and cyclists when designing streets. Previous studies show that 15-minute cities don’t just exist, they will take planning, infrastructure implementation, and possibly most importantly, resident buy-in. It is hard to achieve change in cities without having the support of your residents. Becoming a more walkable Worcester will require municipal staff to convince there is a different and better way to plan our cities and roadways. The City of Worcester is working hard to create community buy-in for micro mobility infrastructure improvements. However, with limited staff and high workloads, it is hard to find time to create and implement comprehensive engagement strategies. It is also time-consuming to analyse the effectiveness of your campaigns after the fact. Just holding a community meeting no longer captures all residents and allows the voices of a few residents to dictate the future of the many. We need innovative plans and strategies that involve new technology and engage residents where they are
ChatGPT or Human Mentors? Student Perceptions of Technology Acceptance and Use and the Future of Mentorship in Higher Education
No full textGAI technologies are increasingly recognized as mentor-like resources in higher education. While these tools offer academic guidance and personalized feedback, little is known about how students perceive and evaluate AI-generated mentorship. This study investigated how Prior ChatGPT Use, primary mentor identity, mentorship effectiveness, and technology acceptance predict students’ response identification and evaluations of AI- versus human-generated responses. College students (N = 127) completed a survey in which they identified the source of masked responses across different domains and rated each response on helpfulness, caring, and likelihood to reach out again. Binary logistic regression models revealed that Prior ChatGPT Use predicted greater accuracy in identifying AI-generated responses, while mentor identity did not. Multiple linear regression analyses showed that students’ evaluations were influenced by perceived response sources more than actual sources. Participants who viewed human mentorship as effective were less likely to seek support from AI-perceived responses, while those who found ChatGPT useful rated AI-perceived responses more favorably. Technology acceptance factors were positively correlated with ratings of AI-perceived responses. These findings suggest that students’ pre-existing biases shape engagement with AI more than content itself, highlighting the importance of perception and the need to promote AI literacy when integrating ChatGPT as a mentorship tool. © 2025 by the authors
Collective Resilience and Resistance
No full textThis chapter explores the relationship between collective resilience and resistance, addressing the conceptual confusion often found in critical scholarship. While these terms are sometimes used interchangeably, the authors argue that not all forms of collective resilience are expressions of resistance. Drawing on research in contexts of colonialism, genocide, and historical oppression, the chapter examines when and how resilience and resistance overlap and when they remain distinct, emphasizing the importance of considering group-based power and the specific nature of the violence involved. © 2025 selection and editorial matter, Yasemin Gülsüm Acar, Blerina Këllezi and Sandra Penić
Women’s cross country group photo, circa 1970s-1980s
No full textGroup photograph of the men\u27s cross country team from Clark University, circa 1970s-1980s.
The photographs in this collection are part of the Photographs and Media record group of Clark University’s Archives & Special Collections.https://commons.clarku.edu/crosscountry/1013/thumbnail.jp
Identification of Factors Correlating to Patient Appointment No-Shows Using Deep & Machine Learning
No full textPatient no-shows, scheduled but unattended medical appointments, pose a significant challenge in healthcare, impacting patient healthcare by disrupting the necessary treatment. Medical appointment no-shows disrupt healthcare providers\u27 schedules that leads to suboptimal patient care. Such issues also lead to increased costs on health care, particularly in the medical field where resources are costly and in great demand. This study explores the factors influencing patient no-shows experienced by a multi-specialty clinic in the USA through the analysis of a dataset with more than 38000 medical records collected over a two-month period in 2019. Using descriptive modeling techniques, we examine key attributes such as patient demographics, appointment types, and scheduling patterns to identify trends associated with appointments missed by the patients. Our analysis reveals 22% of the appointments missed, with significant variations across different ethnic groups, appointment types, and days of the week. Notably, the Hispanic or Latino patient cluster exhibited a higher no-show rate in comparison to the others. Predictive modeling techniques used included Random Forest, Support Vector Machine (SVM), ANN, Gradient Boosting (GBM), and XGBoost to assess the feasibility of forecasting no-shows. The findings provided in this work provide an insight into appointment trends and disparities that may impact decision makers\u27 approach to patient appointment scheduling. © 2025 IEEE