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171.
This study replicated a previous research project addressing connections between family-of-origin communication and organizational dissent expression. We predicted that family communication patterns (FCPs), specifically conversation and conformity orientations, would predict upward, lateral, and displaced dissent. As in the original study, significant findings emerged for upward dissent and conformity orientation. This replication study indicated family type as an inconsistent predictor of expressed dissent. Contrary to the original study, we found no evidence to support a connection between pluralistic family types and upward dissent. Finally, as an extension of the original study we analyzed effects of FCPs on displaced dissent, finding an inverse association with conversation orientation. Implications for future research are discussed. 相似文献
172.
Oh and LaRose (2016) contended that problem severity and channel publicness shape support seekers’ goals, time spent composing messages, and the sophistication of those messages. The current study seeks to replicate and extend Oh and LaRose’s study to produce a more accurate understanding of the factors that influence the sophistication of support-seeking messages online. Support seekers’ goals varied according to the severity of a stressor and the publicness of a channel, and the amount of time people spent composing a message influenced its sophistication. We extend the original study by examining associations that were not initially tested. Time spent composing messages mediated the influence of problem severity on the quality of people’s messages, and this effect was moderated by channel publicness. 相似文献
173.
Margret Beditz 《Publishing Research Quarterly》2018,34(2):157-169
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. 相似文献
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Mohd Roslan Mohd Nor Ummu Zainab Al-Abidah Binti Zainal Abidin Nusaibah Binti Nasaruddin 《Publishing Research Quarterly》2018,34(4):540-553
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. 相似文献
177.
Jiawei Huang Mahda M. Bagher Heather Dohn Ross Nathan Piekielek Jan Oliver Wallgrün Jiayan Zhao 《Journal of Map & Geography Libraries》2018,14(1):40-63
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. 相似文献
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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). 相似文献
180.
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. 相似文献