Scientific collaboration networks in Pakistan and their impact on institutional research performance

Higher education research performance parameters classified by systems theory: antecedents for the development of assessment models

Purpose

This paper aims to identify and classify the parameters that construct the input, processes, output, productivity and outcome variables that affect performance. These parameters are used in the evaluation model to measure research performance in universities so that they can be used as the basis for making leadership policies both at the national and institutional levels.

Design/methodology/approach

The design of this research is a quantitative research method using a survey questionnaire that was sent to the heads of research institutions at universities in Indonesia. To obtain these parameters, a test for determining the value of the loading factor was used.

Findings

The authors found that input variable parameters consisted of 10 parameters; process variable consisted of 22 parameters; output variable parameters consisted of 8 parameters; productivity variable consisted of 4 parameters; and outcome variable parameters consisted of 10 parameters.

Originality/value

One approach to obtain parameters is through systems theory, where every element that makes up the organization contributes to the achievement of goals. This study attempted to develop parameters in the performance appraisal model of systems theory-based research institutions that are adapted to trends in the direction of research in universities. These parameters are based on aspects of input, process, output, productivity and outcome.

Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study’s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach.

Black articles matter: exploring relative deprivation and implicit bias in library and information science research publications of Africa and other continents

Purpose

The purpose of this paper is to explore relative deprivation and implicit bias in library and information science research publications of Africa and other continents.

Design/methodology/approach

Research design used for this study is descriptive survey research. Specifically, the study will adopt both web content analysis and survey to collect data. The content analysis covers the whole continents of the world: Africa, Asia, Eastern Europe, Latin America, Middle East, Northern America, Pacific Region and Western Europe; using the Webometrics World Ranking of Universities and the SCImago/Scopus Journal Ranking. Library and information science was used as the search and control parameter. The scopes covered by the research are: 1. Ascertaining the visible publishing and assessment standards of top library and information science (LIS) journals, which was evaluated using Kleinert and Wager (2010)'s study.

Findings

It was found out among others that editors making fair and unbiased decisions as policy is seen in 33% of the journals, which is very poor. All the structural disparities, such as presence ranking, impact ranking, excellence ranking, etc. were favouring Europe and the Americas mainly. As much as rejection is getting to these respondents, research generally is also suffering by missing out on some untapped knowledge and ideas from these deprived populations. Many authors are losing faith in their capabilities and are now afraid of venturing into tedious research exercises because it will most likely be rejected either ways.

Research limitations/implications

It is an established fact that social media gains research impact and attracts international collaborations. In support, studies such as Hassan et al. (2019) reported the fact that tweet mentions of articles with positive sentiment to more visibility and citations. They claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. In addition, Hassan et al. (2020) equally highlighted tweet coupling as a social media methodology useful for clustering scientific publications. Despite the fact that social media have these influences on research and publications visibility and presence, the context of the present research did cover this scope of study. The study focused mainly on sources from Scopus as well as results from responses. Further studies can be carried out on this area.

Originality/value

Research studies linking “Black Articles Matter” to relative deprivation and implicit bias in research publications, especially in library and information discipline, are very rare. Also, the scope of approach of the study is quite different and interesting.

A bibliometric analysis and science mapping of scientific publications of Alzahra University during 1986–2019

Purpose

Currently, the evaluation of scientific performance of universities is one of the important indicators in various ranking systems. One way to evaluate the academic performance of universities is to analyze the scientific documents of universities in reputable international databases. The purpose of this article is to analyze and evaluate the scientific publications of Alzahra University (Iran) as the top 100–200 universities during 1986–2019.

Design/methodology/approach

This study was performed using bibliometrics and visualization techniques. The Scopus database was used to collect data. Affiliation search and advanced search were used to retrieve the data. Excel, VOSviewer and CRExplorer software were used to analyze the data.

Findings

The results showed that the scientific publications and received citations by Alzahra University documents during the time have been upward. At the national level, it was the most scientific collaboration with researchers at the University of Tehran. Also at the international level, the most scientific collaboration has been with the United States, Canada and Germany. In total, 80% of scientific publications were published by 20% of authors. Also, 70% of the highly cited articles were published in journals with quartile 1. Finally, clustering results showed that Alzahra University's scientific publications are in five main categories, including “chemistry,” “physics,” “biology,” “psychology and educational sciences” and “accounting sciences, management, and computer science.”

Originality/value

This study could be a good model for evaluating the performance of scientific productions of universities and scientific institutions with bibliometrics and visualization approaches.

Native Language Identification of Fluent and Advanced Non-Native Writers

Native Language Identification (NLI) aims at identifying the native languages of authors by analyzing their text samples written in a non-native language. Most existing studies investigate this task for educational applications such as second language acquisition and require the learner corpora. This article performs NLI in a challenging context of the user-generated-content (UGC) where authors are fluent and advanced non-native speakers of a second language. Existing NLI studies with UGC (i) rely on the content-specific/social-network features and may not be generalizable to other domains and datasets, (ii) are unable to capture the variations of the language-usage-patterns within a text sample, and (iii) are not associated with any outlier handling mechanism. Moreover, since there is a sizable number of people who have acquired non-English second languages due to the economic and immigration policies, there is a need to gauge the applicability of NLI with UGC to other languages. Unlike existing solutions, we define a topic-independent feature space, which makes our solution generalizable to other domains and datasets. Based on our feature space, we present a solution that mitigates the effect of outliers in the data and helps capture the variations of the language-usage-patterns within a text sample. Specifically, we represent each text sample as a point set and identify the top- k stylistically similar text samples (SSTs) from the corpus. We then apply the probabilistic k nearest neighbors’ classifier on the identified top- k SSTs to predict the native languages of the authors. To conduct experiments, we create three new corpora where each corpus is written in a different language, namely, English, French , and German . Our experimental studies show that our solution outperforms competitive methods and reports more than 80% accuracy across languages.

StyloThai:

Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic k nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.

Academic social networks and collaboration patterns

Purpose

This study attempts to use a new source of data collection from open government data sets to identify potential academic social networks (ASNs) and defines their collaboration patterns. The purpose of this paper is to propose a direction that may advance our current understanding on how or why ASNs are formed or motivated and influence their research collaboration.

Design/methodology/approach

This study first reviews the open data sets in Taiwan, which is ranked as the first state in Global Open Data Index published by Open Knowledge Foundation to select the data sets that expose the government’s R&D activities. Then, based on the theory review of research collaboration, potential ASNs in those data sets are identified and are further generalized as various collaboration patterns. A research collaboration framework is used to present these patterns.

Findings

Project-based social networks, learning-based social networks and institution-based social networks are identified and linked to various collaboration patterns. Their collaboration mechanisms, e.g., team composition, motivation, relationship, measurement, and benefit-cost, are also discussed and compared.

Originality/value

In traditional, ASNs have usually been known as co-authorship networks or co-inventorship networks due to the limitation of data collection. This study first identifies some ASNs that may be formed before co-authorship networks or co-inventorship networks are formally built-up, and may influence the outcomes of research collaborations. These information allow researchers to deeply dive into the structure of ASNs and resolve collaboration mechanisms.