International Collaboration and Openness in Jordanian Research Output: A 10-year Publications Feedback

Previous reports stressed on the importance of international collaboration and open access (OA) publishing, as they increase the quality of the research and increase its benefits. In this study, we will assess Jordanian international research collaboration and OA publishing in the past 10 years. We performed a Scopus search for Jordanian publications in the years 2008–2017, where we extracted and calculated the total number of publications, publications in OA journals, publications with international collaborations. Moreover, we assessed the disciplines Jordanians usually publish in. During the 10-year interval, we found a total of 20,359 Jordan-affiliated publications indexed in Scopus. We found a dramatic increase in number of publications with international collaboration from 38% in 2008 reaching 53.3% in 2017, and an increase in OA publications from 7.3% of the total publications in 2008, reaching 18.7% of the total publications in 2017. Total number of Scopus indexed publications from Jordan has increased by 57.9% over the past 10 years. Although international collaboration and OA publications have dramatically increased in recent years, Jordanian researchers should focus more on international collaboration with developed countries and on self-archiving their publications.     

The Dynamic Landscape of Marketing Children’s Books: Publishers Find Consistent Success Through a Combination of Online and Traditional Marketing to Adults and Children

There are a number of challenges unique to the children’s book sector faced by publishers in today’s digitally-dominant business market, making it more crucial than ever for children’s book marketers to create multifaceted marketing plans that include differing but complementary approaches. Among the common marketing challenges specific to children’s books are overabundance of content, competition, access to the library market, the digital versus print dichotomy, difficulty of marketing directly to young readers on the Internet, and the dual (child and adult) target audience. While some marketing tactics must effectively attract the attention of the child reader, it is of fundamental importance (with some exceptions) for publishers, authors, and marketers of children’s books to also ensure their marketing techniques target the adult gatekeepers who are likely to purchase those books. Depending upon various factors, including age range of the reader and subject matter, marketers of children’s books are most successful when applying a mix of traditional and digital marketing techniques.     

Features of Islamic Children’s Books in English: A Case Study of Books Published in Malaysia

The purpose of this study is to investigate the elements of Islamic children’s books that are written in English by focusing on books that are available in the Malaysian market and published in Malaysia. This study applies the content analysis method, by examining the selected books of their subject matter as well as the language. The results of the study reveal that Islamic books written for children in English are distinctive in terms of the content and language features.     

The IR Has Two Faces: Positioning Institutional Repositories for Success

This article describes the ongoing efforts at University of Nevada, Las Vegas (UNLV) Libraries to evolve the role of the institutional repository (IR) and to effectively position it within the context of the Libraries’ collections, research support, and scholarly communication services. A major component of this process is re-examining the fundamental aims of the IR and aligning it to the Libraries and the campus strategic goals. The authors situate UNLV Libraries’ experience within the context of the current literature to provide background and reasoning for our decision to pursue two, at times conflicting, aims for the IR: one for scholarly communication and another for research administration.     

Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).