791 research outputs found

    Displacement and the Humanities: Manifestos from the Ancient to the Present

    Get PDF
    This is the final version. Available on open access from MDPI via the DOI in this recordThis is a reprint of articles from the Special Issue published online in the open access journal Humanities (ISSN 2076-0787) (available at: https://www.mdpi.com/journal/humanities/special_issues/Manifestos Ancient Present)This volume brings together the work of practitioners, communities, artists and other researchers from multiple disciplines. Seeking to provoke a discourse around displacement within and beyond the field of Humanities, it positions historical cases and debates, some reaching into the ancient past, within diverse geo-chronological contexts and current world urgencies. In adopting an innovative dialogic structure, between practitioners on the ground - from architects and urban planners to artists - and academics working across subject areas, the volume is a proposition to: remap priorities for current research agendas; open up disciplines, critically analysing their approaches; address the socio-political responsibilities that we have as scholars and practitioners; and provide an alternative site of discourse for contemporary concerns about displacement. Ultimately, this volume aims to provoke future work and collaborations - hence, manifestos - not only in the historical and literary fields, but wider research concerned with human mobility and the challenges confronting people who are out of place of rights, protection and belonging

    Detecting Team Conflict From Multiparty Dialogue

    Get PDF
    The emergence of online collaboration platforms has dramatically changed the dynamics of human teamwork, creating a veritable army of virtual teams composed of workers in different physical locations. The global world requires a tremendous amount of collaborative problem solving, primarily virtual, making it an excellent domain for computer scientists and team cognition researchers who seek to understand the dynamics involved in collaborative tasks to provide a solution that can support effective collaboration. Mining and analyzing data from collaborative dialogues can yield insights into virtual teams\u27 thought processes and help develop virtual agents to support collaboration. Good communication is indubitably the foundation of effective collaboration. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomenon in which humans synchronize their linguistic choices. This dissertation presents several technical innovations in the usage of machine learning towards analyzing, monitoring, and predicting collaboration success from multiparty dialogue by successfully handling the problems of resource scarcity and natural distribution shifts. First, we examine the problem of predicting team performance from embeddings learned from multiparty dialogues such that teams with similar conflict scores lie close to one another in vector space. We extract the embeddings from three types of features: 1) dialogue acts 2) sentiment polarity 3) syntactic entrainment. Although all of these features can be used to predict team performance effectively, their utility varies by the teamwork phase. We separate the dialogues of players playing a cooperative game into stages: 1) early (knowledge building), 2) middle (problem-solving), and 3) late (culmination). Unlike syntactic entrainment, both dialogue act and sentiment embeddings effectively classify team performance, even during the initial phase. Second, we address the problem of learning generalizable models of collaboration. Machine learning models often suffer domain shifts; one advantage of encoding the semantic features is their adaptability across multiple domains. We evaluate the generalizability of different embeddings to other goal-oriented teamwork dialogues. Finally, in addition to identifying the features predictive of successful collaboration, we propose multi-feature embedding (MFeEmb) to improve the generalizability of collaborative task success prediction models under natural distribution shifts and resource scarcity. MFeEmb leverages the strengths of semantic, structural, and textual features of the dialogues by incorporating the most meaningful information from dialogue acts (DAs), sentiment polarities, and vocabulary of the dialogues. To further enhance the performance of MFeEmb under a resource-scarce scenario, we employ synthetic data generation and few-shot learning. We use the method proposed by Bailey and Chopra (2018) for few-shot learning from the FsText python library. We replaced the universal embedding with our proposed multi-feature embedding to compare the performance of the two. For data augmentation, we propose using synonym replacement from collaborative dialogue vocabulary instead of synonym replacement from WordNet. The research was conducted on several multiparty dialogue datasets, including ASIST, SwDA, Hate Speech, Diplomacy, Military, SAMSum, AMI, and GitHub. Results show that the proposed multi-feature embedding is an excellent choice for the meta-training stage of the few-shot learning, even if it learns from a small train set of size as small as 62 samples. Also, our proposed data augmentation method showed significant performance improvement. Our research has potential ramifications for the development of conversational agents that facilitate teaming as well as towards the creation of more effective social coding platforms to better support teamwork between software engineers

    Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward

    Get PDF
    No abstract available

    ‘Inner qualities versus inequalities’: A case study of student change learning about Aboriginal health using sequential, explanatory mixed methods

    Full text link
    Racism and lack of self-determination in health care perpetuate injury and injustice to Aboriginal people. To instil cultural safety at individual, organisational, community and systems levels, a key site of action has been health professional education that seeks to elicit reflexivity, cultural humility and a working understanding of Aboriginal health concepts. Studies in Aboriginal community settings show Family Well Being (FWB) empowerment education is effective in supporting personal and collective reflexivity and transformation through empowering life skills development. Implementation of FWB within educational settings shows early signs of effectiveness among students. Yet knowledge of the steps and processes of student change is lacking. This mixed methods explanatory case study sought to measure and understand change in postgraduate students of a leading Australian university learning about Aboriginal health and wellbeing through blended delivery, including through face-to-face immersion in FWB in an urban classroom. Three interrelated studies investigated fidelity and acceptability of the program, measured and analysed growth and empowerment in students, and explained processes of change observed, through thematic analysis of asynchronous online discussions using lenses based on transformative learning and empowerment. Researcher reflexivity was promoted by Aboriginal supervision. Over six years, 194 students enrolled in two different Aboriginal public health courses, 85 of them in the FWB course. As well as achieving program fidelity and acceptability, pre/post-course change in students across a range of emotional empowerment, personal growth and life-long learning processes was measured in the FWB group. Thematic analysis revealed students’ fluid and recursive processes of transformative learning in their professional selves and capacities to act in domains important to Aboriginal health. This case study contributes new knowledge critical to strengthening health professional capabilities for ever more complex, uncertain and emotionally demanding sites of practice, and to work in empowering ways—with, not for, Aboriginal people and communities

    Women's Leadership in Music: Modes, Legacies, Alliances

    Get PDF
    Various modes of women's contemporary cultural, social and political leadership can be found in music. Informed by different histories and culturally bound social mores but also by a comparative perspective, the contributors of this volume ask what can be considered leadership in culture from women's point of view. They deconstruct the notion of leadership as corporative and career-related modes of success by showing how women's agency, power and negotiation in and through music can and should be considered as empowering, transformative and role-modeling. By interweaving several disciplinary perspectives - from ethnomusicology, musicology and cultural management to sociology and anthropology - this volume aims to substantially contribute to the study of women's leadership

    2015 GREAT Day Program

    Get PDF
    SUNY Geneseo’s Ninth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1009/thumbnail.jp

    COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory

    Get PDF
    Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination

    KOME 11.

    Get PDF
    • …
    corecore