21 research outputs found

    A Probabilistic Approach in Historical Linguistics. Word Order Change in Infinitival Clauses: from Latin to Old French

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    Thesis (Ph.D.) - Indiana University, French, 2015This thesis investigates word order change in infinitival clauses from Object-Verb (OV) to Verb-Object (VO) in the history of Latin and Old French. By applying a variationist approach, I examine a synchronic word order variation in each stage of language change, from which I infer the character, periodization and constraints of diachronic variation. I also show that in discourse-configurational languages, such as Latin and Early Old French, it is possible to identify pragmatically neutral contexts by using information structure annotation. I further argue that by mapping pragmatic categories into a syntactic structure, we can detect how word order change unfolds. For this investigation, the data are extracted from annotated corpora spanning several centuries of Latin and Old French and from additional resources created by using computational linguistic methods. The data are then further codified for various pragmatic, semantic, syntactic and sociolinguistic factors. This study also evaluates previous factors proposed to account for word order alternation and change. I show how information structure and syntactic constraints change over time and propose a method that allows researchers to differentiate a stable word order alternation from alternation indicating a change. Finally, I present a three-stage probabilistic model of word order change, which also conforms to traditional language change patterns

    Topic Analysis through Streamgraph via Shiny Application: A Social Collaborative Approach

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    With the increasing complexity and volume of data, the transformation from streaming information into actionable knowledge becomes more and more challenging and requires a synthesis of computational and substantive approaches. In this view, the collaboration between developers and substantive experts is essential for obtaining meaningful and strategic insights. Despite the large number of various platforms and software to develop a customized tool, the main challenge is developing social organizational forms for communication. In this paper, we explore a new method of organization workflow, namely a social collaboration via the rizzoma platform. In particular, we introduce our on-going project for developing a research-driven visualization portal that is responsive to the need of specific research in strategic studies

    Language Variation Suite: A theoretical and methodological contribution for linguistic data analysis

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    In recent years there has been growing interest in quantitative methods for analyzing linguistic data. Advanced multifactorial statistical analyses, such as inferential trees and mixed-effects logistic regression models, have become more accessible for linguistic research as a result of the availability of an open source programming environment provided by the statistical software R. In the present paper, we introduce a novel toolkit, Language Variation Suite, a software program that offers a friendly environment for conducting quantitative analyses. We demonstrate how theory built on traditional monofactorial analysis can be extended to macro and micro multifactorial approaches allowing for a deeper understanding of language variation. The focus of the analysis is based on intervocalic /d/ deletion in Spanish from the Diachronic Study of the Speech of Caracas 1987 and 2004-2010. In contrast to traditional methodological approaches we have treated intervocalic /d/ as a continuous dependent variable according to the intensity ratio measurements obtained. Furthermore, we have integrated various syntactic, phonetic and sociolinguistic factors. Non-parametric and fixed-effects regression models revealed that overall age (younger speakers), sex (male speakers), phonetic context (low vowels), token frequency and morphosyntactic category (past participles) have a significant effect on the lenition of intervocalic /d/. In contrast, the mixed-effects model selected only phonetic context, frequency and category, showing that individual speaker variation is higher than group variation

    Hiring in the substance use disorder treatment related sector during the first five years of Medicaid expansion

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    Effective treatment strategies exist for substance use disorder (SUD), however severe hurdles remain in ensuring adequacy of the SUD treatment (SUDT) workforce as well as improving SUDT affordability, access and stigma. Although evidence shows recent increases in SUD medication access from expanding Medicaid availability under the Affordable Care Act, it is yet unknown whether these policies also led to a growth in the changes in the nature of hiring in SUDT related workforce, partly due to poor data availability. Our study uses novel data to shed light on recent trends in a fast-evolving and policy-relevant labor market, and contributes to understanding the current SUDT related workforce and the effect of Medicaid expansion on hiring attempts in this sector. We examine attempts over 2010-2018 at hiring in the SUDT and related behavioral health sector as background for estimating the causal effect of the 2014-and-beyond state Medicaid expansion on these outcomes through "difference-in-difference" econometric models. We use Burning Glass Technologies (BGT) data covering virtually all U.S. job postings by employers. Nationally, we find little growth in the sector's hiring attempts in 2010-2018 relative to the rest of the economy or to health care as a whole. However, this masks diverging trends in subsectors, which saw reduction in hospital based hiring attempts, increases towards outpatient facilities, and changes in occupational hiring demand shifting from medical personnel towards counselors and social workers. Although Medicaid expansion did not lead to any statistically significant or meaningful change in overall hiring attempts, there was a shift in the hiring landscape.Comment: 7 figures, 20 page

    Back to Business and (Re)employing Workers? Labor Market Activity During State COVID-19 Reopenings

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    We study the effect of state reopening policies on a large set of labor market indicators through May 2020 to: (1) understand the recent increase in employment using longitudinal as well as cross-sectional data, (2) assess the likely trajectory of reemployment going forward, and (3) investigate the strength of job matches that were disrupted by COVID-19. Estimates from event studies and difference-in-difference regressions suggest that some of the recent increases in employment activity, as measured by cellphone data on work-related mobility, internet searches related to employment, and new and continuing unemployment insurance claims, were likely related to state reopenings, often predating actual reopening dates somewhat. We provide suggestive evidence that increases in employment stem from people returning to their prior jobs: reopenings are only weakly related to job postings, and longitudinal CPS data show that large shares of the unemployed-on-layoff and employed-but-absent in April who transitioned to employment in May remain in the same industry or occupation. Longitudinal CPS estimates further show declines in reemployment probabilities with time away from work. Taken together, these estimates suggest that employment relationships are durable in the short run, but raise concerns that employment gains requiring new employment matches may not be as rapid.Weinberg gratefully acknowledges support from UL1 TR002733 and R24 HD058484

    A Probabilistic Approach in Historical Linguistics Word Order Change in Infinitival Clauses: from Latin to Old French

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    This thesis investigates word order change in infinitival clauses from Object-Verb (OV) to Verb-Object (VO) in the history of Latin and Old French. By applying a variationist approach, I examine a synchronic word order variation in each stage of language change, from which I infer the character, periodization and constraints of diachronic variation. I also show that in discourse-configurational languages, such as Latin and Early Old French, it is possible to identify pragmatically neutral contexts by using information structure annotation. I further argue that by mapping pragmatic categories into a syntactic structure, we can detect how word order change unfolds. For this investigation, the data are extracted from annotated corpora spanning several centuries of Latin and Old French and from additional resources created by using computational linguistic methods. The data are then further codified for various pragmatic, semantic, syntactic and sociolinguistic factors. This study also evaluates previous factors proposed to account for word order alternation and change. I show how information structure and syntactic constraints change over time and propose a method that allows researchers to differentiate a stable word order alternation from alternation indicating a change. Finally, I present a three-stage probabilistic model of word order change, which also conforms to traditional language change patterns
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