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Morris, S.A.: Mapping research specialties (2008)
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- Annual review of information science and technology. 22(1987), S.109-145
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Grudin, J.: Human-computer interaction (2011)
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Campe, P.: Case, semantic roles, and grammatical relations : a comprehensive bibliography (1994)
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Erlandsson, A.: Electronic records management : a literature review (1997)
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- Content
- Auch als: http://www.archives.ca/ica/p-er/english.html
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Rader, H.B.: Library orientation and instruction - 1993 (1994)
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Kling, R.: ¬The Internet and unrefereed scholarly publishing (2003)
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- Abstract
- In the early 1990s, much of the enthusiasm for the use of electronic media to enhance scholarly communication focused an electronic journals, especially electronic-only, (pure) e journals (see for example, Peek & Newby's [1996] anthology). Much of the systematic research an the use of electronic media to enhance scholarly communication also focused an electronic journals. However, by the late 1990s, numerous scientific publishers had transformed their paper journals (p journals) into paper and electronic journals (p-e journals) and sold them via subscription models that did not provide the significant costs savings, speed of access, or breadth of audience that pure e -journal advocates had expected (Okerson, 1996). In 2001, a group of senior life scientists led a campaign to have publishers make their journals freely available online six months after publication (Russo, 2001). The campaign leaders, using the name "Public Library of Science," asked scientists to boycott journals that did not comply with these demands for open access. Although the proposal was discussed in scientific magazines and conferences, it apparently did not persuade any journal publishers to comply (Young, 2002). Most productive scientists, who work for major universities and research institutes
-
Davenport, E.; Hall, H.: Organizational Knowledge and Communities of Practice (2002)
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- Abstract
- A community of practice has recently been defined as "a flexible group of professionals, informally bound by common interests, who interact through interdependent tasks guided by a common purpose thereby embodying a store of common knowledge" (Jubert, 1999, p. 166). The association of communities of practice with the production of collective knowledge has long been recognized, and they have been objects of study for a number of decades in the context of professional communication, particularly communication in science (Abbott, 1988; Bazerman & Paradis, 1991). Recently, however, they have been invoked in the domain of organization studies as sites where people learn and share insights. If, as Stinchcombe suggests, an organization is "a set of stable social relations, dehberately created, with the explicit intention of continuously accomplishing some specific goals or purposes" (Stinchcombe, 1965, p. 142), where does this "flexible" and "embodied" source of knowledge fit? Can communities of practice be harnessed, engineered, and managed like other organizational groups, or does their strength lie in the fact that they operate outside the stable and persistent social relations that characterize the organization?
-
Benoit, G.: Data mining (2002)
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- Abstract
- Data mining (DM) is a multistaged process of extracting previously unanticipated knowledge from large databases, and applying the results to decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then guide decision making and forecast the effects of those decisions. However, this definition may be applied equally to "knowledge discovery in databases" (KDD). Indeed, in the recent literature of DM and KDD, a source of confusion has emerged, making it difficult to determine the exact parameters of both. KDD is sometimes viewed as the broader discipline, of which data mining is merely a component-specifically pattern extraction, evaluation, and cleansing methods (Raghavan, Deogun, & Sever, 1998, p. 397). Thurasingham (1999, p. 2) remarked that "knowledge discovery," "pattern discovery," "data dredging," "information extraction," and "knowledge mining" are all employed as synonyms for DM. Trybula, in his ARIST chapter an text mining, observed that the "existing work [in KDD] is confusing because the terminology is inconsistent and poorly defined.
