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Article

Priyalatha Govindasamy, Kathy E. Green and Antonio Olmos

The Brief Symptom Inventory-18 (BSI-18) is a tool used to measure clinically relevant psychological symptoms to support clinical decision-making at intake and during the…

Abstract

Purpose

The Brief Symptom Inventory-18 (BSI-18) is a tool used to measure clinically relevant psychological symptoms to support clinical decision-making at intake and during the course of treatment in various settings. The BSI-18 has frequently been evaluated for construct validity via analysis of its structure. However, these studies showed mixed results of the factor solutions and no consensus on the dimensionality. Therefore, the purpose of this paper is to synthesize the empirical findings about the factor structure to reach an overall conclusion about the factor structure of the BSI-18.

Design/methodology/approach

A meta-analysis of factor analysis results using an aggregated co-occurrence matrix approach was conducted to synthesize the factor structure. The item factor loading information from seven published studies is gathered, combined and summarized to conclude the factor structure of the instrument. Multidimensional scaling (MDS) was used to quantify the similarity between the underlying factor structures of BSI-18 from different empirical articles.

Findings

The perceptual map from MDS-found items was clustered into three distinctive factors matching the original intent. The findings highlight the consistency of the BSI-18’s factor structure. However, the findings should be used with caution owing to the small sample size and conclusions made from visual representation.

Originality/value

This original study contributes to research in the provision of empirically tested measures that take a focus on factor analysis and the use of meta-analysis technique to account for an understanding of the factor structure.

Details

Mental Health Review Journal, vol. 25 no. 4
Type: Research Article
ISSN: 1361-9322

Keywords

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Article

Yezheng Liu, Lu Yang, Jianshan Sun, Yuanchun Jiang and Jinkun Wang

Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However…

Abstract

Purpose

Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups.

Design/methodology/approach

The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed.

Findings

Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment.

Research limitations/implications

The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts.

Practical implications

The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient.

Social implications

The proposed methods have potential value to improve scientific collaboration and research innovation.

Originality/value

The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.

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Article

Chunjuan Luan and Xiuping Wang

The purpose of this paper is to help China's science and technology (abbr. as S&T) managers and related policy makers to allocate S&T human resources, optimize…

Abstract

Purpose

The purpose of this paper is to help China's science and technology (abbr. as S&T) managers and related policy makers to allocate S&T human resources, optimize organizational systems of laboratories, design and plan some grant projects, and manage other S&T‐related work in the field of nanoscience and nanotechnology, by measuring and mapping of technology‐fields correlation, with nanotechnology as an example.

Design/methodology/approach

Methodologies such as co‐occurrence analysis, correlation analysis, multidimensional scaling (abbr. as MDS) analysis, dendrogram (tree‐like) analysis, etc. are employed to measure and map technology‐fields correlation.

Findings

It is found that the exact relevance degree of any two technology‐fields exists among the top 33 technology‐fields with high frequencies. There are three industrial clusters in Multidimentional Scaling View, that is, nanotechnology used in bio‐medical industry, nanotechnology used in new material industry and nanotechnology used in electronic industry. Hierarchy of any two technology‐fields can be found out in the dendrogram view of the top 33 technology‐fields.

Originality/value

This paper could be of great significance to China's S&T managers and related policy makers, especially in the area of nanotechnology, in selecting and managing generic technology and the findings in this paper can be applied in some other fields of science and technology management in China. Both technology‐fields correlation analysis and MDS and dendrogram view analysis could benefit China's policy makers in managing nanotechnology research and development activities.

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Article

Wei Yu and Junpeng Chen

The purpose of this paper is to explore the potential of enriching the library subject headings with folksonomy for enhancing the visibility and usability of the library…

Abstract

Purpose

The purpose of this paper is to explore the potential of enriching the library subject headings with folksonomy for enhancing the visibility and usability of the library subject headings.

Design/methodology/approach

The WorldCat-million data set and SocialBM0311 are preprocessing and over 210,000 library catalog records and 124,482 non-repeating tags were adopted to construct the matrix to observe the semantic relation between library subject headings and folksonomy. The proposed system is compared with the state-of-the-art methods and the parameters are fixed to obtain effective performance.

Findings

The results demonstrate that by integrating different semantic relations from library subject headings and folksonomy, the system’s performance can be improved compared to the benchmark methods. The evaluation results also show that the folksonomy can enrich library subject headings through the semantic relationship.

Originality/value

The proposed method simultaneous weighted matrix factorization can integrate the semantic relation from the library subject headings and folksonomy into one semantic space. The observation of the semantic relation between library subject headings and social tags from folksonomy can help enriching the library subject headings and improving the visibility of the library subject headings.

Details

The Electronic Library, vol. 38 no. 2
Type: Research Article
ISSN: 0264-0473

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Article

Decoteau J. Irby and Shannon P. Clark

The purpose of this paper is to investigate whether race-specific language use can advance organizational learning about the racialized nature of school problems. The…

Abstract

Purpose

The purpose of this paper is to investigate whether race-specific language use can advance organizational learning about the racialized nature of school problems. The study addressed two questions: first, is teacher use of racial language associated with how they frame school discipline problems during conversational exchanges? Second, what do patterns of associations suggest about racial language use as an asset that may influence an organization’s ability to analyze discipline problems?

Design/methodology/approach

Co-occurrence analysis was used to explore patterns between racial language use and problem analysis during team conversational exchanges regarding school discipline problems.

Findings

When participants used race-specific and race-proxy language, they identified more problems and drew on multiple frames to describe school discipline problems.

Research limitations/implications

This paper substantiates that race-specific language is beneficial for organizational learning.

Practical implications

The findings suggest that leading language communities may be an integral, yet overlooked lever for organizational learning and improvement. Prioritizing actions that promote race-specific conversations among school teams can reveal racism/racial conflict and subsequently increase the potential for change.

Originality/value

This paper combines organizational change and race talk research to highlight the importance of professional talk routines in organizational learning.

Details

Journal of Educational Administration, vol. 56 no. 5
Type: Research Article
ISSN: 0957-8234

Keywords

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Article

E.M. NEEDHAM and K. SPARCK JONES

Recent work at the Cambridge Language Research Unit has been concerned with the development of automatic classification procedures for information retrieval. This has…

Abstract

Recent work at the Cambridge Language Research Unit has been concerned with the development of automatic classification procedures for information retrieval. This has taken the form of research into methods of classification of keywords extracted from documents, with a view to using the classes found for coordinate indexing of technical material. We cannot claim to have solved this problem because the methods we have been able to develop so far cannot be applied on a sufficiently large scale. We have, however, made enough progress to make us feel that this is a fruitful line of research.

Details

Journal of Documentation, vol. 20 no. 1
Type: Research Article
ISSN: 0022-0418

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Article

Pulla Rao Chennamsetty, Guruvareddy Avula and Ramarao Chunduri buchhi

The purpose of the research work is to detect camouflaged objects in autonomous systems of military applications and civilian applications such as detecting insects in…

Abstract

Purpose

The purpose of the research work is to detect camouflaged objects in autonomous systems of military applications and civilian applications such as detecting insects in paddy fields, identifying duplicate products in different texture environments.

Design/methodology/approach

Camouflaged objects detection is performed by smoothing texture with nonlinear models and characterizing with statistical methods to detect the objects.

Findings

There are few challenges in existing camouflaged objects detection due to the complexities involved in the detection process. This work proposes a constructive approach with texture statistical characterization for camouflage detection. The proposed technique is found to be better than existing methods while assessing its performance using precision and recall.

Research limitations/implications

Even though there is lot of research work carried, there are few challenges for autonomous systems in camouflage detection due to the complexities involved in the detection process such as texture modeling and dynamic background problems and environment conditions for autonomous system.

Practical implications

Camouflage detection finds potential applications in security systems, surveillance, military and autonomous systems. The proposed work is implemented in different environments for camouflage detection.

Social implications

Social problems such as image acquisition environment, time of day, desert, forest and grass fields of paddy.

Originality/value

The proposed method detects camouflaged objects in autonomous systems where it is applied for images of different kinds. It is found to be effective on images recorded in battlefield and challenging environments.

Details

International Journal of Intelligent Unmanned Systems, vol. 9 no. 1
Type: Research Article
ISSN: 2049-6427

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Article

David Weatherall

Researchers at NEL have been examining the inspection of paint textures.

Abstract

Researchers at NEL have been examining the inspection of paint textures.

Details

Sensor Review, vol. 6 no. 1
Type: Research Article
ISSN: 0260-2288

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Article

Neil Jacobs

Many and varied information sources are used by researchers and managers across sectors relevant to public policy development. When aggregated, these sources can be…

Abstract

Many and varied information sources are used by researchers and managers across sectors relevant to public policy development. When aggregated, these sources can be described in terms of sector‐specific information landscapes. This paper describes results from a survey that investigated such landscapes and relates them to the working practices of those for whom they were relevant. This is achieved through the use of co‐word or co‐term analysis, a technique derived from actor‐network theory. This technique allows for the production of graphic plots of normalised free text term pairs, which take into account inclusiveness. The results suggest that knowledge communities can be identified by this technique.

Details

Journal of Documentation, vol. 58 no. 5
Type: Research Article
ISSN: 0022-0418

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Book part

Arthur C. Graesser, Nia Dowell, Andrew J. Hampton, Anne M. Lippert, Haiying Li and David Williamson Shaffer

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a…

Abstract

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
ISBN: 978-1-78754-474-1

Keywords

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