Toward a Developmental Science of Politics

In this monograph, we argue for the establishment of a developmental science of politics that describes, explains, and predicts the formation and change of individuals’ political knowledge, attitudes, and behavior beginning in childhood and continuing across the life course. Reflecting our goal of contributing both theoretical conceptualizations and empirical data, we have organized the monograph into two broad sections. In the first section, we outline theoretical contributions that the study of politics may make to developmental science and provide practical reasons that empirical research in the domain of politics is important (e.g., for identifying ways to improve civics education and for encouraging higher voting rates among young adults). We also review major historical approaches to the study of political development and provide an integrative theoretical framework to ground future work. Drawing on Bronfenbrenner's ecological systems model as an organizing scheme and emphasizing social justice issues, we describe how factors rooted in cultural contexts, families, and children themselves are likely to shape political development. In the second section of the monograph, we argue for the importance and utility of studying major political events, such as presidential elections, and introduce the major themes, rationales, and hypotheses for a study of U.S. children's views of the 2016 U.S. presidential election. In addition, we apply a social-justice lens to political thought and participation, addressing the role of gender/sex and race/ethnicity in children's political development broadly, and in their knowledge and views of the 2016 U.S. presidential election specifically. In interviews conducted within the month before and after the election, we examined two overarching categories of children's political attitudes: (a) knowledge, preferences, and expectations about the 2016 election, and (b) knowledge and attitudes concerning gender/sex and politics, particularly relevant for the 2016 election given Hillary Clinton's role as the first female major-party candidate for the presidency. Participants were 187 children (101 girls) between 5 and 11 years of age (M = 8.42 years, SD = 1.45 years). They were recruited from schools and youth organizations in five counties in four U.S. states (Kansas, Kentucky, Texas, and Washington) with varying voting patterns (e.g., Trump voters ranged from 27% to 71% of county voters). The sample was not a nationally representative one, but was racially diverse (35 African American, 50 Latinx, 81 White, and 21 multiracial, Asian American, Middle Eastern, or Native American children). In addition to several child characteristics (e.g., age, social dominance orientation [SDO]), we assessed several family and community characteristics (e.g., child-reported parental interest in the election and government-reported county-level voting patterns, respectively) hypothesized to predict outcome variables. Although our findings are shaped by the nature of our sample (e.g., our participants were less likely to support Trump than children in larger, nationwide samples were), they offer preliminary insights into children's political development. Overall, children in our sample were interested in and knowledgeable about the presidential election (e.g., a large majority identified the candidates correctly and reported some knowledge about their personal qualities or policy positions). They reported more information about Donald Trump's than Hillary Clinton's policies, largely accounted for by the substantial percentage of children (41%) who referred to Trump's immigration policies (e.g., building a wall between the United States and Mexico). Overall, children reported as many negative as positive personal qualities of the candidates, with negative qualities being reported more often for Trump than for Clinton (56% and 18% of children, respectively). Most children (88%) supported Clinton over Trump, a preference that did not vary by participants’ gender/sex or race/ethnicity. In their responses to an open-ended inquiry about their reactions to Trump's win, 63% of children reported negative and 18% reported positive emotions. Latinx children reacted more negatively to the election outcome than did White children. Girls’ and boys’ emotional responses to the election outcome did not differ. Children's personal interest in serving as U.S. president did not vary across gender/sex or racial/ethnic groups (overall, 42% were interested). Clinton's loss of the election did not appear to depress (or pique) girls’ interest in becoming U.S. president. With respect to the role of gender/sex in politics, many children (35%) were ignorant about women's absence from the U.S. presidency. Only a single child was able to name a historical individual who worked for women's civil rights or suffrage. Child characteristics predicted some outcome variables. For example, as expected, older children showed greater knowledge about the candidates than did younger children. Family and community characteristics also predicted some outcome variables. For example, as expected, participants were more likely to support Trump if they perceived that their parents supported him and if Trump received a greater percentage of votes in the children's county of residence. Our data suggest that civic education should be expanded and reformed. In addition to addressing societal problems requiring political solutions, civics lessons should include the histories of social groups’ political participation, including information about gender discrimination and the women's suffrage movement in U.S. political history. Providing children with environments that are rich in information related to the purpose and value of politics, and with opportunities and encouragement for political thought and action, is potentially beneficial for youth and their nations.     

Educational Publishing in China: Status Quo,Problems and Counter Measures

In China, educational publishing is devoted to publishing mainly textbooks and teaching materials. For a long time, education publishing has dominated others in China’s publishing industry. However, after entering the new century, especially in the last 2 years, there has been the impact of changes in the College-Entrance Examination, the Invitation to Bid for Compilation and Publication of Textbooks, government procurement and Scientific and Technological Advancement for supplementary books. Therefore, publishing for education has been losing its previous major role and dominance. For making a leap forward and breakthrough in educational publication, we must open up a new train of thought for the development of educational publishing, we need three distinct transformations: first, change in publishing mode from extensive to intensive growth. Second, transformation from variety-scale to market-scale expansion. Third, transformation from being just a provider of content to one providing both content and services.     

Comparison of Alternative Channels in Cross Media Publishing

This paper attempts a direct comparison of the available alternative channels that can be employed in a cross media publishing scheme. More precisely it examines the content, the publishing speed, and the rhythm of the publishing channels. Interesting conclusions are drawn concerning the characteristics of each channel. A new publishing channel is proposed in order to allow newspapers to directly compete with radio and TV channels.     

Negation recognition in medical narrative reports

Substantial medical data, such as discharge summaries and operative reports are stored in electronic textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. When searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. Hence, negation is a major source of poor precision in medical information retrieval systems. Previous research has shown that negated findings may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We present a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy. The new algorithm can be applied also to further context identification and information extraction tasks.

An analysis on document length retrieval trends in language modeling smoothing

Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors, and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek–Mercer, Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet prior smoothing caters for document length more appropriately than Jelinek–Mercer smoothing which leads to its superior retrieval performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek–Mercer smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek–Mercer, and why the performance of the Jelinek–Mercer method is improved by including a length-based prior.

Discoverability and Access in Book Publishing

Search engine discoverability programs have changed the fundamentals of book marketing by enabling content inside the book to be used as the source of marketing itself via search results inside books. The internet trifecta of search, browse, buy is the end game but the complexities of making this work is explored along with an in-depth overview of the potential upside and difficulties of online content purchase models.     

Experimentation and Innovation in U.S. Publishing Today: Findings from the Book Industry Study Group

In February 2008 the Book Industry Study Group (BISG) conducted a survey of U.S. publishers to determine the extent of technology-driven innovation and experimentation. The survey findings were published alongside a set of 10 case studies in May 2008 as “From experimentation to innovation in the digital age—case studies from the North American Book Industry 2008”. This article, written by Michael Healy, Executive Director of the BISG, summarizes the circumstances that led to the survey and shares the key findings.     

Nearest-biclusters collaborative filtering based on constant and coherent values

Collaborative Filtering (CF) Systems have been studied extensively for more than a decade to confront the “information overload” problem. Nearest-neighbor CF is based either on similarities between users or between items, to form a neighborhood of users or items, respectively. Recent research has tried to combine the two aforementioned approaches to improve effectiveness. Traditional clustering approaches (k-means or hierarchical clustering) has been also used to speed up the recommendation process. In this paper, we use biclustering to disclose this duality between users and items, by grouping them in both dimensions simultaneously. We propose a novel nearest-biclusters algorithm, which uses a new similarity measure that achieves partial matching of users’ preferences. We apply nearest-biclusters in combination with two different types of biclustering algorithms—Bimax and xMotif—for constant and coherent biclustering, respectively. Extensive performance evaluation results in three real-life data sets are provided, which show that the proposed method improves substantially the performance of the CF process.