Search (125 results, page 1 of 7)

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Abstract

In recent years, cultural heritage institutions have been exploring the benefits of applying Linked Open Data to their catalogs and digital materials. Innovative and creative methods have emerged to publish and reuse digital contents to promote computational access, such as the concepts of Labs and Collections as Data. Data quality has become a requirement for researchers and training methods based on artificial intelligence and machine learning. This article explores how the quality of Linked Open Data made available by cultural heritage institutions can be automatically assessed. The results obtained can be useful for other institutions who wish to publish and assess their collections.

Date

22. 6.2023 18:23:31

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Abstract

Evaluating the quality of online health information (OHI) is a major challenge facing consumers. We designed PageGraph, an interface that displays quality indicators and associated values for a webpage, based on credibility evaluation models, the nudge theory, and existing empirical research concerning professionals' and consumers' evaluation of OHI quality. A qualitative evaluation of the interface with 16 participants revealed that PageGraph rendered the information and presentation nudges as intended. It provided the participants with easier access to quality indicators, encouraged fresh angles to assess information credibility, provided an evaluation framework, and encouraged validation of initial judgments. We then conducted a quantitative evaluation of the interface involving 60 participants using a between-subject experimental design. The control group used a regular web browser and evaluated the credibility of 12 preselected webpages, whereas the experimental group evaluated the same webpages with the assistance of PageGraph. PageGraph did not significantly influence participants' evaluation results. The results may be attributed to the insufficiency of the saliency and structure of the nudges implemented and the webpage stimuli's lack of sensitivity to the intervention. Future directions for applying nudges to support OHI evaluation were discussed.

Date

22. 6.2023 18:18:34

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Abstract

Journal lists are instruments to categorize, compare, and assess research and scholarly publications. Our study investigates the remarkable proliferation of such journal lists in China, analyses their underlying values, quality criteria and ranking principles, and specifies how concerns specific to the Chinese research policy and publishing system inform these lists. Discouraged lists of "bad journals" reflect concerns over inferior research publications, but also the involved drain on public resources. Endorsed lists of "good journals" are based on criteria valued in research policy, reflecting the distinctive administrative logic of state-led Chinese research and publishing policy, ascribing worth to scientific journals for its specific national and institutional needs. In this regard, the criteria used for journal list construction are contextual and reflect the challenges of public resource allocation in a market-led publication system. Chinese journal lists therefore reflect research policy changes, such as a shift away from output-dominated research evaluation, the specific concerns about research misconduct, and balancing national research needs against international standards, resulting in distinctly Chinese quality criteria. However, contrasting concerns and inaccuracies lead to contradictions in the "qualify" and "disqualify" binary logic and demonstrate inherent tensions and limitations in journal lists as policy tools.

Date

22. 9.2023 16:39:23

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Source

https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

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Content

"Die Englischsprachige Wikipedia verfügt jetzt über mehr als 6 Millionen Artikel. An zweiter Stelle kommt die deutschsprachige Wikipedia mit 2.3 Millionen Artikeln, an dritter Stelle steht die französischsprachige Wikipedia mit 2.1 Millionen Artikeln (via Researchbuzz: Firehose <https://rbfirehose.com/2020/01/24/techcrunch-wikipedia-now-has-more-than-6-million-articles-in-english/> und Techcrunch <https://techcrunch.com/2020/01/23/wikipedia-english-six-million-articles/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&guccounter=1&guce_referrer=aHR0cHM6Ly9yYmZpcmVob3NlLmNvbS8yMDIwLzAxLzI0L3RlY2hjcnVuY2gtd2lraXBlZGlhLW5vdy1oYXMtbW9yZS10aGFuLTYtbWlsbGlvbi1hcnRpY2xlcy1pbi1lbmdsaXNoLw&guce_referrer_sig=AQAAAK0zHfjdDZ_spFZBF_z-zDjtL5iWvuKDumFTzm4HvQzkUfE2pLXQzGS6FGB_y-VISdMEsUSvkNsg2U_NWQ4lwWSvOo3jvXo1I3GtgHpP8exukVxYAnn5mJspqX50VHIWFADHhs5AerkRn3hMRtf_R3F1qmEbo8EROZXp328HMC-o>). 250120 via digithek ch = #fineBlog s.a.: Angesichts der Veröffentlichung des 6-millionsten Artikels vergangene Woche in der englischsprachigen Wikipedia hat die Community-Zeitungsseite "Wikipedia Signpost" ein Moratorium bei der Veröffentlichung von Unternehmensartikeln gefordert. Das sei kein Vorwurf gegen die Wikimedia Foundation, aber die derzeitigen Maßnahmen, um die Enzyklopädie gegen missbräuchliches undeklariertes Paid Editing zu schützen, funktionierten ganz klar nicht. *"Da die ehrenamtlichen Autoren derzeit von Werbung in Gestalt von Wikipedia-Artikeln überwältigt werden, und da die WMF nicht in der Lage zu sein scheint, dem irgendetwas entgegenzusetzen, wäre der einzige gangbare Weg für die Autoren, fürs erste die Neuanlage von Artikeln über Unternehmen zu untersagen"*, schreibt der Benutzer Smallbones in seinem Editorial <https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost/2020-01-27/From_the_editor> zur heutigen Ausgabe."

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Content

Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.

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Abstract

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.

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Abstract

Currently, web document repositories have been collaboratively created and edited. One of these repositories, Wikipedia, is facing an important problem: assessing the quality of Wikipedia. Existing approaches exploit techniques such as statistical models or machine leaning algorithms to assess Wikipedia article quality. However, existing models do not provide satisfactory results. Furthermore, these models fail to adopt a comprehensive feature framework. In this article, we conduct an extensive survey of previous studies and summarize a comprehensive feature framework, including text statistics, writing style, readability, article structure, network, and editing history. Selected state-of-the-art deep-learning models, including the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTMs) network, CNN-LSTMs, bidirectional LSTMs, and stacked LSTMs, are applied to assess the quality of Wikipedia. A detailed comparison of deep-learning models is conducted with regard to different aspects: classification performance and training performance. We include an importance analysis of different features and feature sets to determine which features or feature sets are most effective in distinguishing Wikipedia article quality. This extensive experiment validates the effectiveness of the proposed model.

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Abstract

Knowledge of ovarian cancer patients' information practices around cancer genetic testing (GT) is needed to inform interventions that promote patient access to GT-related information. We interviewed 21 ovarian cancer patients and survivors who had GT as part of the treatment process and analyzed the transcripts using the qualitative content analysis method. We found that patients' information practices, manifested in their information-seeking mode, information sources utilized, information assessment, and information use, showed three distinct styles: passive, semi-active, and active. Patients with the passive style primarily received information from clinical sources, encountered information, or delegated information-seeking to family members; they were not inclined to assess information themselves and seldom used it to learn or influence others. Women with semi-active and active styles adopted more active information-seeking modes to approach information, utilized information sources beyond clinical settings, attempted to assess the information found, and actively used it to learn, educate others, or advocate GT to family and friends. Guided by the social ecological model, we found multiple levels of influences, including personal, interpersonal, organizational, community, and societal, acting as motivators or barriers to patients' information practice. Based on these findings, we discussed strategies to promote patient access to GT-related information.

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Abstract

The content of master's programs accredited by the American Library Association was examined to assess the status of knowledge organization (KO) as a subject in current training. Data collected show that KO remains very visible in a majority of programs, mainly in the form of required and electives courses focusing on descriptive cataloging, classification, and metadata. Observed tendencies include, however, the recent elimination of the required KO course in several programs, the reality that one third of KO electives listed in course catalogs have not been scheduled in the past three years, and the fact that two-thirds of those teaching KO specialize in other areas of information science.

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Abstract

In light of AI (Artificial Intelligence) and NLP (Natural language processing) technologies, this article examines the feasibility of using AI/NLP models to enhance the subject indexing of digital resources. While BERT (Bidirectional Encoder Representations from Transformers) models are widely used in scholarly communities, the authors assess whether BERT models can be used in machine-assisted indexing in the Project Gutenberg collection, through suggesting Library of Congress subject headings filtered by certain Library of Congress Classification subclass labels. The findings of this study are informative for further research on BERT models to assist with automatic subject indexing for digital library collections.

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Abstract

The surrogate record for a book in the library catalog contains subject headings applied on the basis of literary warrant. To assess the extent to which terms like "Indians of North America" are accurate to the content of the items with that label, we invited the items' creators to critique their surrogate records. In interviews with 38 creators we found consensus against the term "Indians of North America" and identified a periphery of related terms that misrepresent the content of the work, are out of alignment with their scholarly communities, and reproduce settler colonial biases in our library systems.

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Abstract

This paper is the result of a request from Library of Congress leadership to assess pre-coordinated versus post-coordinated subject cataloging. It argues that the disadvantages of pre-coordinated subject strings are perennial and continue to hinder progress, while the advantages of post-coordinated subject cataloging have expanded, resulting in new opportunities to serve the needs of catalogers and end users alike. The consequences of retaining pre-coordinated headings will have long-term impacts that heavily out-weigh the short-term challenges of transitioning to new cataloging practices. By implementing post-coordinated, faceted vocabularies, the Library of Congress will be investing in the future of libraries.

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Abstract

Digital repository migrations present a periodic opportunity to assess metadata quality and to perform strategic enhancements. Lafayette College Libraries implemented OCLC FAST (Faceted Application of Subject Terminology) for its digital image collections as part of a migration from multiple repositories to a single one built on the Samvera Hyrax open-source framework. Application of FAST has normalized subject headings across dissimilar collections in a way that tremendously improves descriptive consistency for staff and discoverability for end users. However, the process of applying FAST headings was complicated by several features of in-scope metadata as well as gaps in available controlled subject authorities.

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Abstract

Recommender systems may accelerate knowledge discovery in many fields. However, their users may be competitors guarding their ideas before publication or for other reasons. We describe a simulation experiment to assess user privacy against targeted attacks, modeling recommendations based on co-access data. The analysis uses an unusually long (14?years) set of anonymized historical data on user-item accesses. We introduce the notions of "visibility" and "discoverability." We find, based on historical data, that the majority of the actions of arXiv users would be potentially "visible" under targeted attack. However, "discoverability," which incorporates the difficulty of actually seeing a "visible" effect, is very much lower for nearly all users. We consider the effect of changes to the settings of the recommender algorithm on the visibility and discoverability of user actions and propose mitigation strategies that reduce both measures of risk.

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Abstract

Statistical significance tests can provide evidence that the observed difference in performance between 2 methods is not due to chance. In information retrieval (IR), some studies have examined the validity and suitability of such tests for comparing search systems. We argue here that current methods for assessing the reliability of statistical tests suffer from some methodological weaknesses, and we propose a novel way to study significance tests for retrieval evaluation. Using Score Distributions, we model the output of multiple search systems, produce simulated search results from such models, and compare them using various significance tests. A key strength of this approach is that we assess statistical tests under perfect knowledge about the truth or falseness of the null hypothesis. This new method for studying the power of significance tests in IR evaluation is formal and innovative. Following this type of analysis, we found that both the sign test and Wilcoxon signed test have more power than the permutation test and the t-test. The sign test and Wilcoxon signed test also have good behavior in terms of type I errors. The bootstrap test shows few type I errors, but it has less power than the other methods tested.

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Abstract

This article addresses the problem of detecting crisis-related messages on social media, in order to improve the situational awareness of emergency services. Previous work focused on developing machine-learning classifiers restricted to specific disasters, such as storms or wildfires. We investigate for the first time methods to detect such messages where the type of the crisis is not known in advance, that is, the data are highly heterogeneous. Data heterogeneity causes significant difficulties for learning algorithms to generalize and accurately label incoming data. Our main contributions are as follows. First, we evaluate the extent of this problem in the context of disaster management, finding that the performance of traditional learners drops by up to 40% when trained and tested on heterogeneous data vis-á-vis homogeneous data. Then, in order to overcome data heterogeneity, we propose a new ensemble learning method, and found this to perform on a par with the Gradient Boosting and AdaBoost ensemble learners. The methods are studied on a benchmark data set comprising 26 disaster events and four classification problems: detection of relevant messages, informative messages, eyewitness reports, and topical classification of messages. Finally, in a case study, we evaluate the proposed methods on a real-world data set to assess its practical value.

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Abstract

Web ad effect evaluation is a challenging problem in web marketing research. Although the analysis of web ad effectiveness has achieved excellent results, there are still some deficiencies. First, there is a lack of an in-depth study of the relevance between advertisements and web content. Second, there is not a thorough analysis of the impacts of users and advertising features on user browsing behaviors. And last, the evaluation index of the web advertisement effect is not adequate. Given the above problems, we conducted our work by studying the observer's behavioral pattern based on multimodal features. First, we analyze the correlation between ads and links with different searching results and further assess the influence of relevance on the observer's attention to web ads using eye-movement features. Then we investigate the user's behavioral sequence and propose the directional frequent-browsing pattern algorithm for mining the user's most commonly used browsing patterns. Finally, we offer the novel use of "memory" as a new measure of advertising effectiveness and further build an advertising memory model with integrated multimodal features for predicting the efficacy of web ads. A large number of experiments have proved the superiority of our method.

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Abstract

Purpose Information literacy and health literacy skills are positively correlated with indicators of quality of life. Assessing these literacies, however, can be daunting - particularly with people that may not respond well to prose-based tools. The purpose of this paper is to use information horizons methodology as a metric that may be reflective of literacies. Design/methodology/approach Following a power analysis to insure statistical significance, a sample of 161 participants was recruited from a university population and given formal, vetted measures of information literacy and health literacy and then was asked to create an information horizons map within a health-related context. The information horizons maps were evaluated in two different ways. First, the number of sources was counted. Then, the quality of sources was factored in. Multiple regression analysis was applied to both metrics as independent variables with the other assessments as dependent variables. Anker, Reinhart, and Feeley's model provided the conceptual framework for the study. Findings Information horizons mapping was not found to have a significant relationship with measures of information literacy. However, there were strong, statistically significant relationships with the measures of health literacy employed in this study. Originality/value Employing information horizons methodology as a means of providing a metric to assess literacies may be helpful in providing a more complete picture of a person's abilities. While the current assessment tools have value, this method has the potential to provide important information about the health literacy of people who are not traditionally well represented by prose-based measures.

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Abstract

This study validates the adaptation of a loyalty scale for the library scenario and recovers the hierarchical nature of the perceived service quality (PSQ) by operationalizing it as a second-order level construct, composed by the determinants of service quality (DSQ) identified by Parasuraman, Zeithaml, and Berry in 1985. Our hypothesis was that DSQ are distinct and complementary dimensions, in opposition to the overlapping of DSQ proposed in the SERVQUAL and LibQUAL+® models. In addition, the influence of PSQ on user loyalty (UL) was investigated. Using structural equation modeling, we analyzed the survey data of 1,028 users of a network of academic libraries and report 2 main findings. First, it was shown that the 10 DSQ are statistically significant for the evaluation of PSQ. Second, we demonstrated the positive effect of PSQ for UL. The model presented may be used as a diagnostic and benchmarking tool for managers, coordinators, and librarians who seek to evaluate and/or assess the quality of the services offered by their libraries, as well as to identify and/or manage the loyalty level of their users.