8,443 research outputs found

    An Exploration of the Relationship Between Disability Status Disclosure, Accommodation Use, and Student Success: Curricular and Co-Curricular Implications

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    Colleges and universities rely on the individualized accommodation process to ensure access for students with disabilities, however, there is ample evidence that educational inequity is pervasive. This study used a critical and comparative quantitative methodology (n=6,500) to investigate data from a large urban community college, analyzing the relationship between final grades and accommodation eligibility and use across academic disciplines and curricular modalities (in-person vs. on-line) to identify implications for the academic success of students with disabilities. Results indicate disability inequity varies across racial identity groups and racial inequity persists across disability status groups. Results also indicate that accommodation may be most impactful for students with lower cumulative grade point averages, students taking courses at the 100 level, students taking online courses, and students taking courses in disciplines such as math. There appear to be benefits to a connection with Disability Services even when students do not notify faculty of their eligibility for accommodation. Recommendations include the inclusion of disability as a demographic within institutional reporting; professional development for faculty, staff, and student leaders that goes beyond compliance to address implications of the intersections of gender, race, identity, and disability; and inclusion of disabled student voices to improve access and inclusion throughout curricular and co-curricular programs and activities

    ACOUSTIC SPEECH MARKERS FOR TRACKING CHANGES IN HYPOKINETIC DYSARTHRIA ASSOCIATED WITH PARKINSON’S DISEASE

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    Previous research has identified certain overarching features of hypokinetic dysarthria associated with Parkinson’s Disease and found it manifests differently between individuals. Acoustic analysis has often been used to find correlates of perceptual features for differential diagnosis. However, acoustic parameters that are robust for differential diagnosis may not be sensitive to tracking speech changes. Previous longitudinal studies have had limited sample sizes or variable lengths between data collection. This study focused on using acoustic correlates of perceptual features to identify acoustic markers able to track speech changes in people with Parkinson’s Disease (PwPD) over six months. The thesis presents how this study has addressed limitations of previous studies to make a novel contribution to current knowledge. Speech data was collected from 63 PwPD and 47 control speakers using an online podcast software at two time points, six months apart (T1 and T2). Recordings of a standard reading passage, minimal pairs, sustained phonation, and spontaneous speech were collected. Perceptual severity ratings were given by two speech and language therapists for T1 and T2, and acoustic parameters of voice, articulation and prosody were investigated. Two analyses were conducted: a) to identify which acoustic parameters can track perceptual speech changes over time and b) to identify which acoustic parameters can track changes in speech intelligibility over time. An additional attempt was made to identify if these parameters showed group differences for differential diagnosis between PwPD and control speakers at T1 and T2. Results showed that specific acoustic parameters in voice quality, articulation and prosody could differentiate between PwPD and controls, or detect speech changes between T1 and T2, but not both factors. However, specific acoustic parameters within articulation could detect significant group and speech change differences across T1 and T2. The thesis discusses these results, their implications, and the potential for future studies

    Reframing data ethics in research methods education: a pathway to critical data literacy

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    This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities

    Identity, Power, and Prestige in Switzerland's Multilingual Education

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    Switzerland is known for its multilingualism, yet not all languages are represented equally in society. The situation is exacerbated by the influx of heritage languages and English through migration and globalization processes which challenge the traditional education system. This study is the first to investigate how schools in Grisons, Fribourg, and Zurich negotiate neoliberal forces leading to a growing necessity of English, a romanticized view on national languages, and the social justice perspective of institutionalizing heritage languages. It uncovers power and legitimacy issues and showcases students' and teachers' complex identities to advocate equitable multilingual education

    Essays in labor economics

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    This dissertation consists of three chapters in labor economics. The first chapter explains why the wage gap between black and white Americans has stalled since 1980, after a period of significant narrowing during the 1960s and 1970s. I argue that routine-biased technological change (RBTC) dampened wage gap convergence in 1980-2000. It had a differential impact across races at different parts of the wage distribution. I present new evidence on occupational patterns by race and on determinants of wage disparities along the wage distribution and rationalize them with an RBTC model in which firms engage in statistical discrimination. I show that, surprisingly, the share of employment in routine-intensive occupations has increased for black workers, in contrast with a significant decrease for white workers. I decompose the wage gap changes using the Oaxaca-RIF method. I show that differences in occupational sorting increased wage disparities, thwarting wage convergence between races at the bottom of the wage distribution. Together, these new empirical findings and model allow us to better understand the mechanisms behind racial disparities at the end of the 20th century. The second chapter (with Costas Cavounidis, Kevin Lang, and Raghav Malhotra) develops a tractable general equilibrium model to explain within- and between-occupation changes in skill use over time. We apply the model to skill-use measures from the third, fourth, and revised fourth editions of the Dictionary of Occupational Titles and data from the 1960, 1970, and 1980 Censuses and March Current Population Surveys. We recover changes in skill productivity by exploiting between-occupation movements. We conclude that finger-dexterity productivity grew rapidly while abstract-skill productivity lagged, a form of ‘skill bias.’ Together with substitutability between abstract and routine inputs, these results explain changes in skill use within occupations. In the third chapter (with Silvia Vannutelli), we exploit the enlargement of the European Union in 2007 to study the consequences for the Italian labor market of the permanent legalization of immigrants from Romania and Bulgaria. We use a unique administrative employer-employee dataset covering the universe of Italy’s private sector workers. We study firms’ responses in terms of personnel choices. We find short-term effects on firm-level employment. Employment increased for EU07 migrants at the expense of natives, accompanied by a rise in hirings and separations for the former. We provide evidence that the findings are mainly driven by the migrants’ change of legal status rather than by the arrival of new workers in the country. We also observe a reduction in per-capita revenues and operative added value, confirming that the legalization of previously undocumented workers likely drives the effects

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    Coincidental Generation

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    Generative AI models are emerging as a versatile tool across diverse industries with applications in synthetic data generation computational art personalization of products and services and immersive entertainment Here we introduce a new privacy concern in the adoption and use of generative AI models that of coincidental generation Coincidental generation occurs when a models output inadvertently bears a likeness to a realworld entity Consider for example synthetic portrait generators which are today deployed in commercial applications such as virtual modeling agencies and synthetic stock photography We argue that the low intrinsic dimensionality of human face perception implies that every synthetically generated face will coincidentally resemble an actual person all but guaranteeing a privacy violation in the form of a misappropriation of likeness
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