1,720,966 research outputs found
Euphemistic Rebranding at the Governmental and Institutional Level: A Comparative Historical Analysis
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Artificial Intelligence and Regulation: Total Quality Management for Mental Health Services
Full text linkPurpose
Artificial Intelligence becomes increasingly embedded in various forms in organizational processes and business activities, transforming the structures that support organizations and industries at global scale. Technological innovations bring the need for regulatory institutions and frameworks to be introduced to monitor and control Artificial Intelligence applications, such as ChatGPT and other similar platforms in their current and future forms. This can ensure excellence, quality management and improved performance, more than ever before. We propose directions for the implementation of a TQM model, normalized in the standards of Industry 5.0, and adapted for the Mental Health Services sector.
Methodology
A Multivocal Literature Review was established to identify, select and evaluate published research. The paper used the EFQM Model to explore approaches in implementing Artificial Intelligence industry regulation.
Findings
We suggest that Artificial Intelligence needs to be regulated, and that this will be beneficial for the development of the quality of the services. Through direction of the regulatory institution, proper implementation of Artificial Intelligence in mental health services leads to business performance effects for the mental health providers and to health improvement outcomes for the patients, and eventually to the transformation of the paradigm of this sector’s services. We suggest that results can be affected by stakeholder’s perceptions.
The advance of Artificial Intelligence is expected to shift paradigms in several sectors.
Research limitations/implications
A limitation of this research is that the paper does not include empirical data which could be modelled to verify the application of the EFQM Model. The main implication stemming is that the AI industry is rapidly developing technological and is expected to become a major factor in the operation of the economies. It will bring on the 5th Industrial Revolution, and the call for it to be regulated has to be considered imminently.
Originality/value
This paper contributes to the literature on regulation of the services of
Artificial Intelligence platforms. Through a Multivocal Literature review, the paper argues for the need for such regulations. Through the use of the EFQM Model, the paper argues for the approach that can be implemented in regulating the Artificial Intelligence industry with the intention to ensure high performance and high quality. We particularly argue for the challenge of combing fast-moving Artificial Intelligence firms and platforms offering mental health services and slow-moving medical bodies which establish practices and protocols. Medical
bodies are identified as key stakeholders restrained by their commitment to uphold deontological ethics
Artificial Intelligence and Regulation: Total Quality Management for Mental Health Services
No full textPurpose
Artificial Intelligence becomes increasingly embedded in various forms in organizational processes and business activities, transforming the structures that support organizations and industries at global scale. Technological innovations bring the need for regulatory institutions and frameworks to be introduced to monitor and control Artificial Intelligence applications, such as ChatGPT and other similar platforms in their current and future forms. This can ensure excellence, quality management and improved performance, more than ever before. We propose directions for the implementation of a TQM model, normalized in the standards of Industry 5.0, and adapted for the Mental Health Services sector.
Methodology
A Multivocal Literature Review was established to identify, select and evaluate published research. The paper used the EFQM Model to explore approaches in implementing Artificial Intelligence industry regulation.
Findings
We suggest that Artificial Intelligence needs to be regulated, and that this will be beneficial for the development of the quality of the services. Through direction of the regulatory institution, proper implementation of Artificial Intelligence in mental health services leads to business performance effects for the mental health providers and to health improvement outcomes for the patients, and eventually to the transformation of the paradigm of this sector’s services. We suggest that results can be affected by stakeholder’s perceptions.
The advance of Artificial Intelligence is expected to shift paradigms in several sectors.
Research limitations/implications
A limitation of this research is that the paper does not include empirical data which could be modelled to verify the application of the EFQM Model. The main implication stemming is that the AI industry is rapidly developing technological and is expected to become a major factor in the operation of the economies. It will bring on the 5th Industrial Revolution, and the call for it to be regulated has to be considered imminently.
Originality/value
This paper contributes to the literature on regulation of the services of
Artificial Intelligence platforms. Through a Multivocal Literature review, the paper argues for the need for such regulations. Through the use of the EFQM Model, the paper argues for the approach that can be implemented in regulating the Artificial Intelligence industry with the intention to ensure high performance and high quality. We particularly argue for the challenge of combing fast-moving Artificial Intelligence firms and platforms offering mental health services and slow-moving medical bodies which establish practices and protocols. Medical
bodies are identified as key stakeholders restrained by their commitment to uphold deontological ethics
Artificial Intelligence and Regulation : Total Quality Management for Mental Health Services
Full text linkArtificial Intelligence becomes increasingly embedded in various forms in organizational processes and business activities, transforming the structures that support organizations and industries at global scale. The advance of Artificial Intelligence is expected to shift paradigms in several sectors. Technological innovations bring the need for regulatory institutions and frameworks to be introduced to monitor and control Artificial Intelligence applications, such as ChatGPT and other similar platforms in their current and future forms. This paper contributes to the literature on regulation of the services of Artificial Intelligence platforms. Through a Multivocal Literature review, the paper argues for the need for such regulations. We suggest that Artificial Intelligence needs to be regulated, and that this will be beneficial for the development of the quality of the services. Through direction of the regulatory institution, proper implementation of Artificial Intelligence in mental health services leads to business performance effects for the mental health providers and to health improvement outcomes for the patients, and eventually to the transformation of the paradigm of this sector’s services. We propose directions for the implementation of a TQM model, normalized in the standards of Industry 5.0, and adapted for the Mental Health Services sector. Through the EFQM Model, the paper argues for the approach that can be implemented in regulating the Artificial Intelligence industry to ensure high performance and quality assurance. We suggest that results can be affected by stakeholder’s perceptions, and we focus on the challenge of fast-moving Artificial Intelligence mental health services platforms coexisting as stakeholders with slow-moving medical bodies which establish practices and protocols. Medical bodies are identified as key stakeholders restrained by their commitment to uphold deontological ethics
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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