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.     

From Archive,to Access,to Experience––Historical Documents as a Basis for Immersive Experiences

Libraries have been the key to preserving culture and historic legacy for centuries. One such treasure cataloged in The Pennsylvania State University (Penn State) Libraries is a collection of over 33,000 Sanborn? Fire Insurance Maps. Originally kept safe in metal drawers, the library has embarked on a journey to digitize this abundance of information, combine it with other media such as photographs, and make it accessible through a web interface. Inspired by these efforts, we accessed this information and took it to the next level. Using state of the art 3D modeling and immersive technologies, we created a historic 3D model and immersive experiences of Penn State, exemplarily for the 1922 campus. The resulting experiences can be accessed through the web but also through head mounted displays (HMDs) and mobile phones in combination with VR viewers such as the Google Cardboard. Additionally, they can be used anywhere in the world or on the campus itself as a way to enable remote and in situ experiences and learning. Immersive experiences let us connect to the past, the present and the future, and as such offer value to digital cultural heritage efforts.     

Setting a Non-Agenda: Effects of a Perceived Lack of Problems in Recent News or Twitter

The mere perception that news has given certain problems more coverage can lead the audience to assume that those problems are more important. Given that the news media, at times, obsesses over relatively trivial matters, and given that the audience is increasingly able to filter media exposure, it is worth asking what happens when audience members perceive that recent media coverage has not emphasized any very important problems. In such cases, audience members might assume that any problems facing the nation must not be particularly important. We explicate this attitude of political complacency, test whether perceived media agendas lacking important problems can influence it, and explore whether complacency helps explain political disengagement. We also explore whether these effects generalize beyond news, to new media gatekeepers such as Twitter. Two experiments tested effects of a perceived absence of important problems in recent news or Twitter content. In the case of news, but not Twitter, this increased complacency in both studies. Study 2 added a no-exposure control and found that effects on complacency were driven by the cueing of nonproblem stories, not by the absence of problem story cues. Both studies validated complacency as a predictor of political disengagement.     

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).     

Clustering small-sized collections of short texts

The need to cluster small text corpora composed of a few hundreds of short texts rises in various applications; e.g., clustering top-retrieved documents based on their snippets. This clustering task is challenging due to the vocabulary mismatch between short texts and the insufficient corpus-based statistics (e.g., term co-occurrence statistics) due to the corpus size. We address this clustering challenge using a framework that utilizes a set of external knowledge resources that provide information about term relations. Specifically, we use information induced from the resources to estimate similarity between terms and produce term clusters. We also utilize the resources to expand the vocabulary used in the given corpus and thus enhance term clustering. We then project the texts in the corpus onto the term clusters to cluster the texts. We evaluate various instantiations of the proposed framework by varying the term clustering method used, the approach of projecting the texts onto the term clusters, and the way of applying external knowledge resources. Extensive empirical evaluation demonstrates the merits of our approach with respect to applying clustering algorithms directly on the text corpus, and using state-of-the-art co-clustering and topic modeling methods.     

Imagining transformative spaces: the personal–political sites of community archives

Although much has been written about formal archival spaces, little scholarship has addressed the physical spaces of community archives. This paper asks: How do community members imagine the physical spaces that steward identity-based community archives? Based on focus groups with more than 54 community archives users at five different community archives sites across Southern California, this paper examines how members of marginalized communities conceive of the physical space inhabited by community archives representing their communities. The sites explored range from a prominent location on a university campus, to storefronts, strip malls, and small cinderblock buildings. Yet across sites, users spoke about community archives spaces as symbolic and affectively moving locations. Many users described their community archives site as a “home-away-from-home,” marked by intergenerational dialog and a profound sense of belonging. For other users, community archives sites were described as “politically generative spaces” which foster dialog and debate about identity, representation, and activism and enable the community to envision its future. And yet, while the very existence of community archives is political, many participants felt that the full political potential of these sites is not yet realized. By listening to the voices of the communities represented and served by community archives, our research both indicates that a shift is warranted in archival metaphors of space and reveals how community archives are personally and politically transformative spaces for the communities they represent and serve.     

Traveling through: exploring doctoral demographics in archival studies

The growth of archival studies programs has prompted archival scholars to establish an international network for supporting collaborative research, curriculum development, and pedagogy. Doctoral education is key to the sustainability of such programs and the continuation of the network over time. We carried out longitudinal research to survey the population of doctoral students attending one or more Archival Education and Research Institutes (AERI), an annual meeting first held in 2009. Building on prior research on graduate archival education, we gathered demographic and qualitative data about doctoral students specializing in archival studies who are based in several countries including the USA. We sought to assess attendee motivations, guide conference planning, and help advance overall AERI objectives. Our study provides a baseline understanding of the disciplinary backgrounds, research directions, and specific professional development activities that doctoral students in archival studies pursued around the globe from 2013 to 2015. This paper argues that doctoral education should continue to be a particular subject of archival research and indicates how archival students’ range of academic interests is diversifying and strengthening the scholarly community.     

Decolonising higher education curricula in South Africa: factoring in archives through public programming initiatives

South African higher education curricula are largely Eurocentric, to such an extent that indigenous knowledge is marginalised (Horsthemke in Transform High Educ 2(1)–9, 2017). Consequently, the decolonisation of university curricula has become a necessity. The nationwide ‘FeesMustFall’ student protests in 2015 and 2016 have underlined the need to address this matter urgently. Free quality education and the decolonisation of university curricula were among some of the students’ demands (Le Grange in SAJHE 30(2):1–12, 2016). Fundamentally, decolonising curricula involve a serious investigation of history and the strategising of future actions (Ngulube in Historia 47(2):563–582, 2002). Little is known about the potential role of archives in the process of decolonising higher education curricula in South Africa. Perhaps this can be attributed to a lack of awareness about archives and their significance in South Africa (Sulej in ESARBICA J 33:13–35, 2014). This paper explores the role of archives in the decolonisation of higher education curricula in South Africa. It appears that sub-Saharan scholars rarely consult archives (Onyancha et al. in ESARBICA J 32:67–77, 2013). Therefore, public programming is investigated as a means to get more members of the academic community to use archives as centres of critical inquiry. The relevant literature was consulted and discussed. Unique outreach or public programming initiatives will help the academic community to better understand the significance of archives in the decolonisation process.