6,020 research outputs found

    An examination of the verbal behaviour of intergroup discrimination

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    This thesis examined relationships between psychological flexibility, psychological inflexibility, prejudicial attitudes, and dehumanization across three cross-sectional studies with an additional proposed experimental study. Psychological flexibility refers to mindful attention to the present moment, willing acceptance of private experiences, and engaging in behaviours congruent with one’s freely chosen values. Inflexibility, on the other hand, indicates a tendency to suppress unwanted thoughts and emotions, entanglement with one’s thoughts, and rigid behavioural patterns. Study 1 found limited correlations between inflexibility and sexism, racism, homonegativity, and dehumanization. Study 2 demonstrated more consistent positive associations between inflexibility and prejudice. And Study 3 controlled for right-wing authoritarianism and social dominance orientation, finding inflexibility predicted hostile sexism and racism beyond these factors. While showing some relationships, particularly with sexism and racism, psychological inflexibility did not consistently correlate with varied prejudices across studies. The proposed randomized controlled trial aims to evaluate an Acceptance and Commitment Therapy intervention to reduce sexism through enhanced psychological flexibility. Overall, findings provide mixed support for the utility of flexibility-based skills in addressing complex societal prejudices. Research should continue examining flexibility integrated with socio-cultural approaches to promote equity

    On Wondering: The Epistemology of A Questioning Attitude

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    An emerging trend in contemporary epistemology departs from the traditional preoccupation with the nature of knowledge, belief, evidence, justification, and the problems of skepticism. This trend focuses instead on the nature of inquiry itself and especially on the role of questions and questioning attitudes that arise in and define that activity. Naturally, this emerging trend calls for a philosophical exploration of the nature of questioning attitudes like curiosity and wondering, and of the various epistemological considerations pertaining to them. Consequently, this project primarily addresses two questions: what does it mean to wonder? And what is required to wonder well? The project is thus both descriptive and normative, aiming to pin down the place that wondering has in our ontology of epistemologically significant mental states and to determine what kinds of prescriptive norms it is subject to in the course of rational inquiry

    Explainable text-based features in predictive models of crowdfunding campaigns

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    Reward-Based Crowdfunding offers an opportunity for innovative ventures that would not be supported through traditional financing. A key problem for those seeking funding is understanding which features of a crowdfunding campaign will sway the decisions of a sufficient number of funders. Predictive models of fund-raising campaigns used in combination with Explainable AI methods promise to provide such insights. However, previous work on Explainable AI has largely focused on quantitative structured data. In this study, our aim is to construct explainable models of human decisions based on analysis of natural language text, thus contributing to a fast-growing body of research on the use of Explainable AI for text analytics. We propose a novel method to construct predictions based on text via semantic clustering of sentences, which, compared with traditional methods using individual words and phrases, allows complex meaning contained in the text to be operationalised. Using experimental evaluation, we compare our proposed method to keyword extraction and topic modelling, which have traditionally been used in similar applications. Our results demonstrate that the sentence clustering method produces features with significant predictive power, compared to keyword-based methods and topic models, but which are much easier to interpret for human raters. We furthermore conduct a SHAP analysis of the models incorporating sentence clusters, demonstrating concrete insights into the types of natural language content that influence the outcome of crowdfunding campaigns

    Patterns and Variation in English Language Discourse

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    The publication is reviewed post-conference proceedings from the international 9th Brno Conference on Linguistics Studies in English, held on 16–17 September 2021 and organised by the Faculty of Education, Masaryk University in Brno. The papers revolve around the themes of patterns and variation in specialised discourses (namely the media, academic, business, tourism, educational and learner discourses), effective interaction between the addressor and addressees and the current trends and development in specialised discourses. The principal methodological perspectives are the comparative approach involving discourses in English and another language, critical and corpus analysis, as well as identification of pragmatic strategies and appropriate rhetorical means. The authors of papers are researchers from the Czech Republic, Italy, Luxembourg, Serbia and Georgia

    Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history

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    Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach

    Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions

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    Generics express generalizations about the world (e.g., birds can fly) that are not universally true (e.g., newborn birds and penguins cannot fly). Commonsense knowledge bases, used extensively in NLP, encode some generic knowledge but rarely enumerate such exceptions and knowing when a generic statement holds or does not hold true is crucial for developing a comprehensive understanding of generics. We present a novel framework informed by linguistic theory to generate exemplars -- specific cases when a generic holds true or false. We generate ~19k exemplars for ~650 generics and show that our framework outperforms a strong GPT-3 baseline by 12.8 precision points. Our analysis highlights the importance of linguistic theory-based controllability for generating exemplars, the insufficiency of knowledge bases as a source of exemplars, and the challenges exemplars pose for the task of natural language inference.Comment: EACL 202

    Essays on nonsampling errors in household panel surveys

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    Household surveys represent the predominant form of data collection in low- and middle-income countries and function as crucial substitutes to constrained administrative data. In recent years, following an increasing demand for data, researchers and policymakers alike have addressed the continued issue of low-quality data. While much progress has been made, many sources of data, including household surveys, have been identified as being insufficiently accurate and reliable, thus constraining informed decision-making on behalf of policymakers. Indeed, the importance of obtaining high-quality outputs has been recognised in the Sustainable Development Goals, which emphasise that to date, data is key to informing policy, monitoring progress, and ultimately achieving formulated goals. This thesis aims to provide a better understanding of survey methodological issues in low- and middle-income countries and provide an outlook on the future of panel survey applications. Thereby, the first two essays deal with identification of nonsampling errors in household survey datasets, factors influencing their prevalence, and their impact. Conversely, the third essay examines the continued role of agriculture in rural development. The first essay investigates the prevalence of nonsampling errors in the seventh survey wave of a long-term household panel survey conducted in Thailand and Vietnam, which encompasses 3,812 households. An analysis of the distribution of nonsampling errors is undertaken in order to ascertain which type of error is most prevalent in the underlying computerised survey instrument. These findings are then compared with those of an earlier study, which examined the prevalence of nonsampling errors in a paper-based survey instrument. Thereafter, a negative binomial model is applied to analyse factors influencing nonsampling errors, which simultaneously assesses the influence of the interviewer, respondent, and interview and survey environment. The second essay utilises data from the same panel, albeit making use of the longitudinal nature of data. Using seven waves of panel survey data from Thailand, which were collected between 2007 and 2019, interviews of 1,542 identical households were examined with a focus on the consistency of reported employments. A three-stage approach is developed to identify inconsistent reporting thereof between pairs of consecutive survey waves. Additionally, a two-stage multilevel logistic model is applied in order to analyse interviewer and employment characteristics that influence inconsistent reporting. Further, the impact of inconsistent reporting on policy pertaining to household welfare is examined. The third essay utilises three waves of household survey data from Thailand, which were conducted in 2007, 2013, and 2019, and considers 1,160 identical households. A descriptive analysis is undertaken in which changes in livelihoods of rural households in Northeast Thailand are examined. Further, a logit regression is applied to identify factors influencing poverty incidence, which differentiates by the typology of household based on the importance of agriculture. The first essay finds that computerised survey instruments have a substantially lower count of missing data, whereas measurement errors remain a pressing issue. The findings of the negative binomial regression model highlight the importance of interviewer training and indicate that more outgoing and sympathetic interviewers produce interviews of higher quality. Additionally, conditions of the interview and survey are shown to influence the prevalence of nonsampling errors. Notably, the results suggest that measurement errors are most likely to occur in initial survey weeks, whereas the likelihood of refusal increases as the survey progresses. In Vietnam, incongruence of ethnicity between interviewers and respondents indicated a substantial increase in nonsampling errors. Further, survey providers in endeavours to collect high-quality data must account for differences in survey implementation. The second essay identifies substantial cases of underreporting of employments throughout pairs of consecutive survey waves. Notably, informal employments are less likely to be consistently reported and more complex household compositions are positively correlated with inconsistency. The impact of omitted employments on welfare indicators is demonstrated to be substantial with poverty headcounts being overestimated by, on average 6.7 percentage points at the provincial level. The third essay highlights that while income has been observed to increase over a 12-year period, which has coincided with an increasing proportion of agriculture-based households being classified as non-poor, little has changed in rural livelihoods in rural Northeast Thailand. Despite substantial out-migration of working-aged household members, most households remain engaged in agriculture and can be described as part-time, small-scale farmers. Further, those households mainly engaged in agriculture are observed to become increasingly dependent on government interventions due to the region’s propensity to droughts. In conclusion, the essays examining data quality of household surveys in Thailand and Vietnam provide new perspectives regarding factors that survey providers must consider in conducting surveys. Further, shortcomings of labour modules that are typically used in household surveys in developing countries are identified and provide an entry point to a debate on possible approaches to more accurately collecting employment data. The third essay highlights that rural populations remain highly reliant on agriculture and that the role of agriculture in development cannot be understated

    Schizophrenia research under the framework of predictive coding: body, language, and others

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    Although there have been so many studies on schizophrenia under the framework of predictive coding, works focusing on treatment are very preliminary. A model-oriented, operationalist, and comprehensive understanding of schizophrenia would promote the therapy turn of further research. We summarize predictive coding models of embodiment, co-occurrence of over- and under-weighting priors, subjective time processing, language production or comprehension, self-or-other inference, and social interaction. Corresponding impairments and clinical manifestations of schizophrenia are reviewed under these models at the same time. Finally, we discuss why and how to inaugurate a therapy turn of further research under the framework of predictive coding
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