Validating the Willingness to Self-Censor Scale: Individual Differences in the Effect of the Climate of Opinion on Opinion Expression

Hayes, Glynn, and Shanahan (2005) defined self-censorship asthe withholding of one’s opinion around an audience perceivedto disagree with that opinion. They argued that people differin their willingness to self-censor and introduced an 8-itemself-report instrument, the Willingness to Self-Censor scale,to measure this individual difference. The results of an experimentalstudy presented here provide further evidence of the constructvalidity of the scale. Each participant in the study was presentedwith a hypothetical scenario that contained information suggestinga group of people the participant was conversing with abouta controversial topic held opinions that were either uniformlysimilar to or different from the participant’s own opinion.Four weeks prior, each participant had responded to the Willingnessto Self-Censor scale and a measure of dispositional shyness.As expected, the manipulation of the climate of opinion affectedwillingness to express an opinion to the group, but more soamong those who scored relatively high on the Willingness toSelf-Censor scale. These results support the notion that somepeople rely on information about the climate of opinion moreso than do others when they decide whether or not to voice theiropinion publicly, and they suggest that the Willingness to Self-Censorscale measures this individual difference.     

Investigation of an Egyptian Mummy board by Using Clinical Multi-slice Computed Tomography

A modern approach to the diagnosis of artistic heritage makes use of non-destructive techniques which avoid damage to artworks or signs of interaction with the analytical technique used. For this reason, today, the employment of X-rays to study artistic heritage is quite widespread. Computed tomography (CT) Imaging was first used as specialized medical diagnostic tool in the early 1970s. Today, CT scanners have become more common, and non-destructive three-dimensional imaging has found an increasing number of uses in different areas such as materials research, geology, archaeology, and museum conservation. In this work, a new generation of clinical CT (based on the Dual Energy and multi-slice acquisition principle) is used to investigate an Egyptian wooden mummy board with a total acquisition time of 93.23 seconds and a voxel size of 0.078?×?0.078?×?1.5?mm. The use of two distinct techniques of image post-processing, the two-dimensional multi-planar reconstruction (MPR) and the three-dimensional volume rendering technique (VRT) allowed us to: evaluate the state of conservation of the mummy board, determine how the wooden object was implemented, recognize a nineteenth-century intervention of consolidation, and advance the hypothesis that the wooden planks were reused. In particular, we highlight the use of the cardiovascular and pulmonary filter with VRT reconstruction, which allows a virtual elimination of different parts of the mummy board to investigate those areas otherwise accessible only by removing parts of the board and thus damaging the work.     

Lighting the Shroud of Turin for Exhibition in 2015

ABSTRACT

Lighting a cultural heritage artifact requires balancing visual perception with preventive conservation, by providing the best lighting (in terms of spectral distribution and quantity) to enable the viewer to appreciate details and color, while limiting photo-induced degradation. The paper outlines the methodology applied by a multi-disciplinary team while lighting the Shroud of Turin at its last public exhibition in 2015. The methodology considered the special requirements of the Shroud, including exposure to ultraviolet light, while providing appropriate display conditions that would meet audience expectations. The desired appearance (readability of the body image and color) was defined with the help of Shroud researchers and confirmed by subjective tests, while appropriate light levels for preservation were set in agreement with standard requirements and using knowledge of the degradation of linen in visible and UV light. The installation provided a controlled environment and a managed visitor route to the Shroud, assuring excellent perception of both details and color, with the lowest illuminance level about 15?lx.     

Reasons for Student Engagement in Extra-Class Communication

This study extends instructional communication research on extra-class communication (ECC) by presenting an integrative analysis comparing the explanatory utility of student- and instructor-oriented reasons for student engagement in frequent ECC. Results from 495 college students suggested that student-oriented reasons, specifically the relational and functional student motives, were the only significant, positive predictors of students’ perceptions of frequent ECC engagement. Other student motives (excuse making, participation, sycophancy) and perceptions of instructor approachability did not significantly predict perceived frequency of ECC engagement.     

Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.     

Scaling up Content Analysis

ABSTRACT

Employing a number of different standalone programs is a prevalent approach among communication scholars who use computational methods to analyze media content. For instance, a researcher might use a specific program or a paid service to scrape some content from the Web, then use another program to process the resulting data, and finally conduct statistical analysis or produce some visualizations in yet another program. This makes it hard to build reproducible workflows, and even harder to build on the work of earlier studies. To improve this situation, we propose and discuss four criteria that a framework for automated content analysis should fulfill: scalability, free and open source, adaptability, and accessibility via multiple interfaces. We also describe how to put these considerations into practice, discuss their feasibility, and point toward future developments.     

Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics

The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses.