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Are Contributions from Chinese Physicists Undercited?

Abstract

Purpose

In this work, we want to examine whether or not there are some scientific fields to which contributions from Chinese scholars have been under or over cited.

Design/methodology/approach

We do so by comparing the number of received citations and the IOF of publications in each scientific field from each country. The IOF is calculated from applying the modified closed system input–output analysis (MCSIOA) to the citation network. MCSIOA is a PageRank-like algorithm which means here that citations from the more influential subfields are weighted more towards the IOF.

Findings

About 40% of subfields in physics in China are undercited, meaning that their net influence ranks are higher (better) than the direct rank, while about 75% of subfields in the USA and German are undercited.

Research limitations

Only APS data is analyzed in this work. The expected citation influence is assumed to be represented by the IOF, and this can be wrong.

Practical implications

MCSIOA provides a measure of net influences and according to that measure. Overall, Chinese physicists’ publications are more likely overcited rather than being undercited.

Originality/value

The issue of under or over cited has been analyzed in this work using MCSIOA.

Open access
CiteOpinion: Evidence-based Evaluation Tool for Academic Contributions of Research Papers Based on Citing Sentences

Abstract

Purpose

To uncover the evaluation information on the academic contribution of research papers cited by peers based on the content cited by citing papers, and to provide an evidence-based tool for evaluating the academic value of cited papers.

Design/methodology/approach

CiteOpinion uses a deep learning model to automatically extract citing sentences from representative citing papers; it starts with an analysis on the citing sentences, then it identifies major academic contribution points of the cited paper, positive/negative evaluations from citing authors and the changes in the subjects of subsequent citing authors by means of Recognizing Categories of Moves (problems, methods, conclusions, etc.), and sentiment analysis and topic clustering.

Findings

Citing sentences in a citing paper contain substantial evidences useful for academic evaluation. They can also be used to objectively and authentically reveal the nature and degree of contribution of the cited paper reflected by citation, beyond simple citation statistics.

Practical implications

The evidence-based evaluation tool CiteOpinion can provide an objective and in-depth academic value evaluation basis for the representative papers of scientific researchers, research teams, and institutions.

Originality/value

No other similar practical tool is found in papers retrieved.

Research limitations

There are difficulties in acquiring full text of citing papers. There is a need to refine the calculation based on the sentiment scores of citing sentences. Currently, the tool is only used for academic contribution evaluation, while its value in policy studies, technical application, and promotion of science is not yet tested.

Open access
Identification of Sarcasm in Textual Data: A Comparative Study

Abstract

Purpose

Ever increasing penetration of the Internet in our lives has led to an enormous amount of multimedia content generation on the internet. Textual data contributes a major share towards data generated on the world wide web. Understanding people’s sentiment is an important aspect of natural language processing, but this opinion can be biased and incorrect, if people use sarcasm while commenting, posting status updates or reviewing any product or a movie. Thus, it is of utmost importance to detect sarcasm correctly and make a correct prediction about the people’s intentions.

Design/methodology/approach

This study tries to evaluate various machine learning models along with standard and hybrid deep learning models across various standardized datasets. We have performed vectorization of text using word embedding techniques. This has been done to convert the textual data into vectors for analytical purposes. We have used three standardized datasets available in public domain and used three word embeddings i.e Word2Vec, GloVe and fastText to validate the hypothesis.

Findings

The results were analyzed and conclusions are drawn. The key finding is: the hybrid models that include Bidirectional LongTerm Short Memory (Bi-LSTM) and Convolutional Neural Network (CNN) outperform others conventional machine learning as well as deep learning models across all the datasets considered in this study, making our hypothesis valid.

Research limitations

Using the data from different sources and customizing the models according to each dataset, slightly decreases the usability of the technique. But, overall this methodology provides effective measures to identify the presence of sarcasm with a minimum average accuracy of 80% or above for one dataset and better than the current baseline results for the other datasets.

Practical implications

The results provide solid insights for the system developers to integrate this model into real-time analysis of any review or comment posted in the public domain. This study has various other practical implications for businesses that depend on user ratings and public opinions. This study also provides a launching platform for various researchers to work on the problem of sarcasm identification in textual data.

Originality/value

This is a first of its kind study, to provide us the difference between conventional and the hybrid methods of prediction of sarcasm in textual data. The study also provides possible indicators that hybrid models are better when applied to textual data for analysis of sarcasm.

Open access
Infrastructure of Scientometrics: The Big and Network Picture

Abstract

A network is a set of nodes connected via edges, with possibly directions and weights on the edges. Sometimes, in a multi-layer network, the nodes can also be heterogeneous. In this perspective, based on previous studies, we argue that networks can be regarded as the infrastructure of scientometrics in the sense that networks can be used to represent scientometric data. Then the task of answering various scientometric questions related to this data becomes an algorithmic problem in the corresponding network.

Open access
Masked Sentence Model Based on BERT for Move Recognition in Medical Scientific Abstracts

Abstract

Purpose

Move recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units. To improve the performance of move recognition in scientific abstracts, a novel model of move recognition is proposed that outperforms the BERT-based method.

Design/methodology/approach

Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences. In this paper, inspired by the BERT masked language model (MLM), we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition. Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps. Then, we compare our model with HSLN-RNN, BERT-based and SciBERT using the same dataset.

Findings

Compared with the BERT-based and SciBERT models, the F1 score of our model outperforms them by 4.96% and 4.34%, respectively, which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-the-art results of HSLN-RNN at present.

Research limitations

The sequential features of move labels are not considered, which might be one of the reasons why HSLN-RNN has better performance. Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed, which is a typical biomedical database, to fine-tune our model.

Practical implications

The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences.

Originality/value

T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way. The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.

Open access
A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence

Abstract

Purpose

To reveal the research hotpots and relationship among three research hot topics in biomedicine, namely CRISPR, i PS (induced Pluripotent Stem) cell and Synthetic biology.

Design/methodology/approach

We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.

Findings

The results reveal the main research hotspots in the three topics are different, but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.

Research limitations

All analyses use keywords, without any other forms.

Practical implications

We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions, and for promoting biomedical developments.

Originality/value

We chose the core keywords in three research hot topics in biomedicine by using h-index.

Open access
Effective Factors on Project Success in Malaysian Construction Industry

Abstract

Although success is a word that encapsulates a general and wide idea and it is challenging to provide a definite and a consensus definition from all individuals concerned, for many years, there has been a growing interest in identification of the success factors and the relationship with project success. In this research, the main objective investigates the relationship between top management, project mission, personnel, communication and Schedule/Plan and project success in construction industry in Malaysia. A survey was conducted among Managers and Employees of construction companies registered with Construction Industry Development Board (CIDB) of Malaysia and the correlation and regression analysis was carried out in order to test the hypotheses of the study. Key areas are now offered that practitioners and academics should further explore to contribute to the knowledge body on project success and to explore in more details which factors affect project success in construction industry in Malaysia.

Open access
An Automatic Method to Identify Citations to Journals in News Stories: A Case Study of UK Newspapers Citing Web of Science Journals

Abstract

Purpose

Communicating scientific results to the public is essential to inspire future researchers and ensure that discoveries are exploited. News stories about research are a key communication pathway for this and have been manually monitored to assess the extent of press coverage of scholarship.

Design/methodology/Approach

To make larger scale studies practical, this paper introduces an automatic method to extract citations from newspaper stories to large sets of academic journals. Curated ProQuest queries were used to search for citations to 9,639 Science and 3,412 Social Science Web of Science (WoS) journals from eight UK daily newspapers during 2006–2015. False matches were automatically filtered out by a new program, with 94% of the remaining stories meaningfully citing research.

Findings

Most Science (95%) and Social Science (94%) journals were never cited by these newspapers. Half of the cited Science journals covered medical or health-related topics, whereas 43% of the Social Sciences journals were related to psychiatry or psychology. From the citing news stories, 60% described research extensively and 53% used multiple sources, but few commented on research quality.

Research Limitations

The method has only been tested in English and from the ProQuest Newspapers database.

Practical implications

Others can use the new method to systematically harvest press coverage of research.

Originality/value

An automatic method was introduced and tested to extract citations from newspaper stories to large sets of academic journals.

Open access
Disclosing and Evaluating Artistic Research

Abstract

Purpose

This study expands on the results of a stakeholder-driven research project on quality indicators and output assessment of art and design research in Flanders—the Northern, Dutch-speaking region of Belgium. Herein, it emphasizes the value of arts & design output registration as a modality to articulate the disciplinary demarcations of art and design research.

Design/methodology/approach

The particularity of art and design research in Flanders is first analyzed and compared to international examples. Hereafter, the results of the stakeholder-driven project on the creation of indicators for arts & design research output assessment are discussed.

Findings

The findings accentuate the importance of allowing an assessment culture to emerge from practitioners themselves, instead of imposing ill-suited methods borrowed from established scientific evaluation models (Biggs & Karlsson, 2011)—notwithstanding the practical difficulties it generates. They point to the potential of stakeholder-driven approaches for artistic research, which benefits from constructing a shared metadiscourse among its practitioners regarding the continuities and discontinuities between “artistic” and “traditional” research, and the communal goals and values that guide its knowledge production (Biggs & Karlsson, 2011; Hellström, 2010; Ysebaert & Martens, 2018).

Research limitation

The central limitation of the study is that it focuses exclusively on the “Architecture & Design” panel of the project, and does not account for intra-disciplinary complexities in output assessment.

Practical implications

The goal of the research project is to create a robust assessment system for arts & design research in Flanders, which may later guide similar international projects.

Originality/value

This study is currently the only one to consider the productive potential of (collaborative) PRFSs for artistic research.

Open access
Measuring Societal Impact Is as Complex as ABC

Abstract

Purpose

This paper describes an alternative way of assessing journals considering a broader perspective of its impact. The Area-based connectedness (ABC) to society of journals applied here contributes to the assessment of the dissemination task of journals but with more data it may also contribute to the assessment of other missions.

Design/methodology/approach

The ABC approach assesses the performance of research actors, in this case journals, considering the characteristics of the research areas in which they are active. Each paper in a journal inherits the characteristics of its area. These areas are defined by a publication-based classification. The characteristics of areas relate to 5 dimensions of connectedness to society (news, policy, industrial R&D, technology and local interest) and are calculated by bibliometric indicators and social media metrics.

Findings

In the paper, I illustrate the approach by showing the results for a few journals. They illustrate the diverse profiles that journals may have. We are able to provide a profile for each journal in the Web of Science database. The profiles we present show an appropriate view on the journals’ societal connectedness.

Research limitations

The classification I apply to perform the analyses is a CWTS in house classification based on Web of Science data. As such the application depends on the (updates of) that system. The classification is available at www.leidenranking.com

Practical implications

The dimensions of connectedness discussed in this paper relate to the dissemination task of journals but further development of this method may provide more options to monitor the tasks/mission of journals.

Originality/value

The ABC approach is a unique way to assess performance or impact of research actors considering the characteristics of the areas in which output is published and as such less prone to manipulation or gaming.

Open access