1,053 research outputs found
Supplemental Material - Outcomes From Healthy Eating and Physical Activity Recognition Programs in Early Child Care and Education: A Scoping Review
No full textSupplementary Material for Outcomes From Healthy Eating and Physical Activity Recognition Programs in Early Child Care and Education: A Scoping Review by Katherine R. Arlinghaus, Mary Schroeder, Abby Gold, Lenora P. Goodman, Gerit Wagner, Molly Pass, Shanda Hunt and Jamie Stang in American Journal of Health Promotion</p
Two dimensions of prominence
No full textWagner P, Portele T. Two dimensions of prominence. In: Proceedings of the ESCA Workshop on Dialogue and Prosody. Eindhoven, The Netherlands; 1999.Prosody fulfills a variety of functions in dialogues. Our study examines the relationship between different levels of perceived prominence of syllables and the linguistic and paralinguistic categories accent and emphasis which are conveyed prosodically.
It is still unclear, how a notational system might look like that is able to capture the fine–grained differences between both. The notion of perceptual prominence—defined as a relational parameter on a scale between 0 and 31—seems to be a useful phonetic measure to capture both the subtle differences and shared characteristics
of the phenomena commonly referred to as linguistic and
paralinguistic. Our data indicate that the overall level of prominence within an utterance reflects the level of emphasis, whereas the relative difference of prominences to each other distinguishes between different linguistic accent types
Explaining and Distinguishing Scientific Impact in Information Systems Research: A Study of Review Articles and Design Science Research
No full textSince its inception, the Information Systems discipline has been striving to develop impactful papers that contribute to cumulative knowledge development. Yet, there is a surprising lack of insights on how scientific impact can be accomplished and to which extent this impact represents a substantial engagement with, and extension of the knowledge contributions of the original papers. Especially for review articles and design science research, there are both competing conceptions of what makes these papers impactful and a lack of empirical evidence that would inform this debate. Furthermore, there is a latent skepticism as to whether this sometimes staggering impact of review articles actually represents knowledge development. In a similar way, it is unclear how and to which extent design science research has stimulated meaningful, cumulative knowledge development in information systems. The goal of this thesis is therefore to (1) explain and to (2) distinguish the scientific impact of review articles and design science research. Specifically, the first goal considers overall scientific impact as the dependent variable whose association with antecedent factors is analyzed by regression methodologies. The second goal zooms in on the concept of scientific impact and considers it as a relation between citing and cited papers that is explored through methodologies of manual content analysis and machine learning classification. With Paper 1, I develop the foundation of knowledge development through review articles by crystallizing their contributions and aligning them with their underlying knowledge conversion processes in an overarching framework. This framework is based on the abstraction and codification of knowledge and thereby integrates two essential dimensions of knowledge development. Overall, the foundation developed in the first paper informs the underlying conception of knowledge development of both review articles and citing papers. Addressing the first goal, Papers 2 and 3 develop and test scientometric impact models explaining the scientific impact of review articles and design science research, respectively. Beyond common control variables related to the journal and author level, they offer distinct insights for each type of paper. For review articles, I identify strong effects related to methodological transparency and the development of a research agenda, which vary depending on the type of review. For design science research, I show that theorization and novelty drive scientific impact. Concerning the second goal, Papers 4 and 5 distinguish different types of scientific impact of review articles and design science research, respectively. To analyze the different types of impact that review articles have on their overwhelming number of citing papers, I develop machine learning classifiers. Specifically, I distinguish ideational impact, which corresponds to a substantial engagement with and development of the knowledge contributions of the review article, from perfunctory impact, which corresponds to more trivial connections to the review article. In a similar, though not automated way, I analyze the types of impact of information systems design theories, a particular type of design science research. These analyses primarily focus on whether follow-up research tests and extends these theories. Based on our content analysis, I identify an alarming paucity of follow-up research in this area and develop specific guidelines for the design science community to address this challenge. The thesis concludes with an overview of the research contributions, implications for research practice, future research opportunities, and final remarks
A Woman's All embracing of "the Other". Etty Hillesum as the Basis of a Pedagogy of care and attention
No full textBibDedupe : An Open-Source Python Library for Bibliographic Record Deduplication
No full textBibDedupe is a Python library developed for bibliographic record deduplication in meta-analysis and research synthesis. It is constructed with a focus on four requirements: (1) Zero false positives: The primary objective is to prevent incorrectly merging distinct entries. This focus on zero false positives is crucial to ensure trustworthiness and prevent biased conclusions in the analysis. (2) Reproducibility: BibDedupe implements fixed rules to produce consistent results, in line with the scientific standard of reproducibility. (3) Efficiency: The library is also tuned for low false-negative rates and rapid processing, to ensure scalability of the duplicate identification process. (4) Continuous evaluation and improvement: It is continuously evaluated on over 160,000 records from 10 datasets to ensure its effectiveness, especially in follow-up refinements. Unlike general-purpose deduplication tools, BibDedupe is specifically designed for the unique requirements of bibliographic data in meta-analysis and research synthesis. In this context, BibDedupe aims to provide a Python library that improves the effectiveness and efficiency of duplicate identification, potentially benefitting review papers across scientific disciplines
BibDedupe: An Open-Source Python Library for Bibliographic Record Deduplication
No full text<ul>
<li>Release for JOSS</li>
</ul>If you use this software, please cite it as below
Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis
No full textWhich factors affect the scientific impact of review papers in IS research? : A scientometric study
No full textWhy Do People Keep Consulting Physicians Online? A Study Of Teleconsultation Continuance Intention
No full textTowards an Economic Approach to Identity and Access Management Systems Using Decision Theory
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