Search (55 results, page 3 of 3)

0.0014873719 = product of:
  0.008924231 = sum of:
    0.008924231 = weight(_text_:in in 3162) [ClassicSimilarity], result of:
      0.008924231 = score(doc=3162,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.15028831 = fieldWeight in 3162, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3162)
  0.16666667 = coord(1/6)

Abstract

MELIBEA is a directory of institutional open-access policies for research output that uses a composite formula with eight weighted conditions to estimate the "strength" of open access (OA) mandates (registered in ROARMAP). We analyzed total Web of Science-(WoS)-indexed publication output in years 2011-2013 for 67 institutions in which OA was mandated to estimate the mandates' effectiveness: How well did the MELIBEA score and its individual conditions predict what percentage of the WoS-indexed articles is actually deposited in each institution's OA repository, and when? We found a small but significant positive correlation (0.18) between the MELIBEA "strength" score and deposit percentage. For three of the eight MELIBEA conditions (deposit timing, internal use, and opt-outs), one value of each was strongly associated with deposit percentage or latency ([a] immediate deposit required; [b] deposit required for performance evaluation; [c] unconditional opt-out allowed for the OA requirement but no opt-out for deposit requirement). When we updated the initial values and weights of the MELIBEA formula to reflect the empirical association we had found, the score's predictive power for mandate effectiveness doubled (0.36). There are not yet enough OA mandates to test further mandate conditions that might contribute to mandate effectiveness, but the present findings already suggest that it would be productive for existing and future mandates to adopt the three identified conditions so as to maximize their effectiveness, and thereby the growth of OA.

0.0014724231 = product of:
  0.008834538 = sum of:
    0.008834538 = weight(_text_:in in 3711) [ClassicSimilarity], result of:
      0.008834538 = score(doc=3711,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.14877784 = fieldWeight in 3711, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3711)
  0.16666667 = coord(1/6)

Abstract

It is an industry like no other, with profit margins to rival Google - and it was created by one of Britain's most notorious tycoons: Robert Maxwell. "Even scientists who are fighting for reform are often not aware of the roots of the system: how, in the boom years after the second world war, entrepreneurs built fortunes by taking publishing out of the hands of scientists and expanding the business on a previously unimaginable scale. And no one was more transformative and ingenious than Robert Maxwell, who turned scientific journals into a spectacular money-making machine that bankrolled his rise in British society."

0.0012881019 = product of:
  0.007728611 = sum of:
    0.007728611 = weight(_text_:in in 1194) [ClassicSimilarity], result of:
      0.007728611 = score(doc=1194,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1301535 = fieldWeight in 1194, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1194)
  0.16666667 = coord(1/6)

Abstract

Open access (OA) is free, unrestricted access to electronic versions of scholarly publications. For peer-reviewed journal articles, there are two main routes to OA: publishing in OA journals (gold OA) or archiving of article copies or manuscripts at other web locations (green OA). This study focuses on summarizing and extending current knowledge about green OA. A synthesis of previous studies indicates that green OA coverage of all published journal articles is approximately 12%, with substantial disciplinary variation. Typically, green OA copies become available after considerable time delays, partly caused by publisher-imposed embargo periods, and partly by author tendencies to archive manuscripts only periodically. Although green OA copies should ideally be archived in proper repositories, a large share is stored on home pages and similar locations, with no assurance of long-term preservation. Often such locations contain exact copies of published articles, which may infringe on the publisher's exclusive rights. The technical foundation for green OA uploading is becoming increasingly solid largely due to the rapid increase in the number of institutional repositories. The number of articles within the scope of OA mandates, which strongly influence the self-archival rate of articles, is nevertheless still low.

0.0012881019 = product of:
  0.007728611 = sum of:
    0.007728611 = weight(_text_:in in 1542) [ClassicSimilarity], result of:
      0.007728611 = score(doc=1542,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1301535 = fieldWeight in 1542, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1542)
  0.16666667 = coord(1/6)

Abstract

This paper reviews the worldwide growth of open-access (OA) repositories, 2005 to 2012, using data collected by the OpenDOAR project. Initial repository development was focused on North America, Western Europe, and Australasia, particularly the United States, United Kingdom, Germany, and Australia, followed by Japan. Since 2010, there has been repository growth in East Asia, South America, and Eastern Europe, especially in Taiwan, Brazil, and Poland. During the period, some countries, including France, Italy, and Spain, have maintained steady growth, whereas other countries, notably China and Russia, have experienced limited growth. Globally, repositories are predominantly institutional, multidisciplinary and English-language based. They typically use open-source OAI-compliant software but have immature licensing arrangements. Although the size of repositories is difficult to assess accurately, available data indicate that a small number of large repositories and a large number of small repositories make up the repository landscape. These trends are analyzed using innovation diffusion theory, which is shown to provide a useful explanatory framework for repository adoption at global, national, organizational, and individual levels. Major factors affecting both the initial development of repositories and their take-up include IT infrastructure, cultural factors, policy initiatives, awareness-raising activity, and usage mandates. Mandates are likely to be crucial in determining future repository development.

0.0012881019 = product of:
  0.007728611 = sum of:
    0.007728611 = weight(_text_:in in 2850) [ClassicSimilarity], result of:
      0.007728611 = score(doc=2850,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1301535 = fieldWeight in 2850, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2850)
  0.16666667 = coord(1/6)

Abstract

Research articles disseminate the knowledge produced by the scientific community. Access to this literature is crucial for researchers and the general public. Apparently, "bibliogifts" are available online for free from text-sharing platforms. However, little is known about such platforms. What is the size of the underlying digital libraries? What are the topics covered? Where do these documents originally come from? This article reports on a study of the Library Genesis platform (LibGen). The 25 million documents (42 terabytes) it hosts and distributes for free are mostly research articles, textbooks, and books in English. The article collection stems from isolated, but massive, article uploads (71%) in line with a "biblioleaks" scenario, as well as from daily crowdsourcing (29%) by worldwide users of platforms such as Reddit Scholar and Sci-Hub. By relating the DOIs registered at CrossRef and those cached at LibGen, this study reveals that 36% of all DOI articles are available for free at LibGen. This figure is even higher (68%) for three major publishers: Elsevier, Springer, and Wiley. More research is needed to understand to what extent researchers and the general public have recourse to such text-sharing platforms and why.

0.0012881019 = product of:
  0.007728611 = sum of:
    0.007728611 = weight(_text_:in in 3155) [ClassicSimilarity], result of:
      0.007728611 = score(doc=3155,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1301535 = fieldWeight in 3155, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3155)
  0.16666667 = coord(1/6)

Abstract

With the rapid development of social media, spontaneously user-generated content such as tweets and forum posts have become important materials for tracking people's opinions and sentiments online. A major hurdle for current state-of-the-art automatic methods for sentiment analysis is the fact that human communication often involves the use of sarcasm or irony, where the author means the opposite of what she/he says. Sarcasm transforms the polarity of an apparently positive or negative utterance into its opposite. Lack of naturally occurring utterances labeled for sarcasm is one of the key problems for the development of machine-learning methods for sarcasm detection. We report on a method for constructing a corpus of sarcastic Twitter messages in which determination of the sarcasm of each message has been made by its author. We use this reliable corpus to compare sarcastic utterances in Twitter to utterances that express positive or negative attitudes without sarcasm. We investigate the impact of lexical and pragmatic factors on machine-learning effectiveness for identifying sarcastic utterances and we compare the performance of machine-learning techniques and human judges on this task.

0.0012881019 = product of:
  0.007728611 = sum of:
    0.007728611 = weight(_text_:in in 3768) [ClassicSimilarity], result of:
      0.007728611 = score(doc=3768,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1301535 = fieldWeight in 3768, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3768)
  0.16666667 = coord(1/6)

Abstract

Although peer-review and citation counts are commonly used to help assess the scholarly impact of published research, informal reader feedback might also be exploited to help assess the wider impacts of books, such as their educational or cultural value. The social website Goodreads seems to be a reasonable source for this purpose because it includes a large number of book reviews and ratings by many users inside and outside of academia. To check this, Goodreads book metrics were compared with different book-based impact indicators for 15,928 academic books across broad fields. Goodreads engagements were numerous enough in the arts (85% of books had at least one), humanities (80%), and social sciences (67%) for use as a source of impact evidence. Low and moderate correlations between Goodreads book metrics and scholarly or non-scholarly indicators suggest that reader feedback in Goodreads reflects the many purposes of books rather than a single type of impact. Although Goodreads book metrics can be manipulated, they could be used guardedly by academics, authors, and publishers in evaluations.

0.0012881019 = product of:
  0.007728611 = sum of:
    0.007728611 = weight(_text_:in in 4015) [ClassicSimilarity], result of:
      0.007728611 = score(doc=4015,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1301535 = fieldWeight in 4015, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4015)
  0.16666667 = coord(1/6)

Abstract

Within science, citation counts are widely used to estimate research impact but publication delays mean that they are not useful for recent research. This gap can be filled by Mendeley reader counts, which are valuable early impact indicators for academic articles because they appear before citations and correlate strongly with them. Nevertheless, it is not known how Mendeley readership counts accumulate within the year of publication, and so it is unclear how soon they can be used. In response, this paper reports a longitudinal weekly study of the Mendeley readers of articles in 6 library and information science journals from 2016. The results suggest that Mendeley readers accrue from when articles are first available online and continue to steadily build. For journals with large publication delays, articles can already have substantial numbers of readers by their publication date. Thus, Mendeley reader counts may even be useful as early impact indicators for articles before they have been officially published in a journal issue. If field normalized indicators are needed, then these can be generated when journal issues are published using the online first date.

0.0012881019 = product of:
  0.007728611 = sum of:
    0.007728611 = weight(_text_:in in 4987) [ClassicSimilarity], result of:
      0.007728611 = score(doc=4987,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1301535 = fieldWeight in 4987, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4987)
  0.16666667 = coord(1/6)

Abstract

Recently, social media has become a potentially new way for scholarly journals to disseminate and evaluate research outputs. Scholarly journals have started promoting their research articles to a wide range of audiences via social media platforms. This article aims to investigate the social media presence of scholarly journals across disciplines. We extracted journals from Web of Science and searched for the social media presence of these journals on Facebook and Twitter. Relevant metrics and content relating to the journals' social media accounts were also crawled for data analysis. From our results, the social media presence of scholarly journals lies between 7.1% and 14.2% across disciplines; and it has shown a steady increase in the last decade. The popularity of scholarly journals on social media is distinct across disciplines. Further, we investigated whether social media metrics of journals can predict the Journal Impact Factor (JIF). We found that the number of followers and disciplines have significant effects on the JIF. In addition, a word co-occurrence network analysis was also conducted to identify popular topics discussed by scholarly journals on social media platforms. Finally, we highlight challenges and issues faced in this study and discuss future research directions.

0.0012620769 = product of:
  0.0075724614 = sum of:
    0.0075724614 = weight(_text_:in in 3592) [ClassicSimilarity], result of:
      0.0075724614 = score(doc=3592,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.12752387 = fieldWeight in 3592, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=3592)
  0.16666667 = coord(1/6)

Abstract

We present the results of running 4 different papers through the automated filtering system used by the open access preprint server "arXiv" to classify papers and implement quality control barriers. The exercise was carried out in order to assess whether these highly sophisticated, state-of-the-art filters can distinguish between papers that are controversial or have gone past their "sell-by date," and otherwise normal papers. We conclude that not even the arXiv filters, which are otherwise successful in filtering fringe-topic papers, can fully acquire "Domain-Specific Discrimination" and thus distinguish technical papers that are taken seriously by an expert community from those that are not. Finally, we discuss the implications this has for citizen and policy-maker engagement with the Primary Source Knowledge of a technical domain.

0.0011898974 = product of:
  0.0071393843 = sum of:
    0.0071393843 = weight(_text_:in in 2986) [ClassicSimilarity], result of:
      0.0071393843 = score(doc=2986,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.120230645 = fieldWeight in 2986, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=2986)
  0.16666667 = coord(1/6)

Source

Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff

0.0010517307 = product of:
  0.006310384 = sum of:
    0.006310384 = weight(_text_:in in 944) [ClassicSimilarity], result of:
      0.006310384 = score(doc=944,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.10626988 = fieldWeight in 944, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=944)
  0.16666667 = coord(1/6)

Abstract

Delayed open access (OA) refers to scholarly articles in subscription journals made available openly on the web directly through the publisher at the expiry of a set embargo period. Although a substantial number of journals have practiced delayed OA since they started publishing e-versions, empirical studies concerning OA have often overlooked this body of literature. This study provides comprehensive quantitative measurements by identifying delayed OA journals and collecting data concerning their publication volumes, embargo lengths, and citation rates. Altogether, 492 journals were identified, publishing a combined total of 111,312 articles in 2011; 77.8% of these articles were made OA within 12 months from publication, with 85.4% becoming available within 24 months. A journal impact factor analysis revealed that delayed OA journals have citation rates on average twice as high as those of closed subscription journals and three times as high as immediate OA journals. Overall, the results demonstrate that delayed OA journals constitute an important segment of the openly available scholarly journal literature, both by their sheer article volume and by including a substantial proportion of high-impact journals.

0.0010517307 = product of:
  0.006310384 = sum of:
    0.006310384 = weight(_text_:in in 5317) [ClassicSimilarity], result of:
      0.006310384 = score(doc=5317,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.10626988 = fieldWeight in 5317, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5317)
  0.16666667 = coord(1/6)

Abstract

Open-access mega-journals (OAMJs) are characterized by their large scale, wide scope, open-access (OA) business model, and "soundness-only" peer review. The last of these controversially discounts the novelty, significance, and relevance of submitted articles and assesses only their "soundness." This article reports the results of an international survey of authors (n = 11,883), comparing the responses of OAMJ authors with those of other OA and subscription journals, and drawing comparisons between different OAMJs. Strikingly, OAMJ authors showed a low understanding of soundness-only peer review: two-thirds believed OAMJs took into account novelty, significance, and relevance, although there were marked geographical variations. Author satisfaction with OAMJs, however, was high, with more than 80% of OAMJ authors saying they would publish again in the same journal, although there were variations by title, and levels were slightly lower than subscription journals (over 90%). Their reasons for choosing to publish in OAMJs included a wide variety of factors, not significantly different from reasons given by authors of other journals, with the most important including the quality of the journal and quality of peer review. About half of OAMJ articles had been submitted elsewhere before submission to the OAMJ with some evidence of a "cascade" of articles between journals from the same publisher.

8.9242304E-4 = product of:
  0.005354538 = sum of:
    0.005354538 = weight(_text_:in in 3438) [ClassicSimilarity], result of:
      0.005354538 = score(doc=3438,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.09017298 = fieldWeight in 3438, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=3438)
  0.16666667 = coord(1/6)

Abstract

Font-rendering technologies play a critical role in presenting clear and aesthetic fonts to enhance the experience of reading from computer screens. This article presents three studies investigating visual and psychological correlates of people's preferences toward different onscreen text enhancements such as ClearType developed by Microsoft. Findings suggested that (a) people's acuity and hue sensitivity were two major factors that affect their preferences to ClearType's color filtering of subpixels on fonts, and (b) specific personality traits such as disagreeableness also could correlate with people's impressions of different onscreen text enhancements that were used. These empirical data would inform digital typographers and human-computer interaction scientists who aim to develop better systems of onscreen reading.

7.4368593E-4 = product of:
  0.0044621155 = sum of:
    0.0044621155 = weight(_text_:in in 3766) [ClassicSimilarity], result of:
      0.0044621155 = score(doc=3766,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.07514416 = fieldWeight in 3766, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3766)
  0.16666667 = coord(1/6)

Abstract

SlideShare is a free social website that aims to help users distribute and find presentations. Owned by LinkedIn since 2012, it targets a professional audience but may give value to scholarship through creating a long-term record of the content of talks. This article tests this hypothesis by analyzing sets of general and scholarly related SlideShare documents using content and citation analysis and popularity statistics reported on the site. The results suggest that academics, students, and teachers are a minority of SlideShare uploaders, especially since 2010, with most documents not being directly related to scholarship or teaching. About two thirds of uploaded SlideShare documents are presentation slides, with the remainder often being files associated with presentations or video recordings of talks. SlideShare is therefore a presentation-centered site with a predominantly professional user base. Although a minority of the uploaded SlideShare documents are cited by, or cite, academic publications, probably too few articles are cited by SlideShare to consider extracting SlideShare citations for research evaluation. Nevertheless, scholars should consider SlideShare to be a potential source of academic and nonacademic information, particularly in library and information science, education, and business.