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Journal articles on the topic 'Knowledge'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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1

Tiwari, Veena, and Praveen Pandit. "Knowledge, Knowledge Society and National Knowledge Network." Indian Journal of Applied Research 3, no. 8 (October 1, 2011): 359–60. http://dx.doi.org/10.15373/2249555x/aug2013/114.

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2

STAPLEFORD, SCOTT. "Locke on Sensitive Knowledge as Knowledge1." Theoria 75, no. 3 (September 2009): 206–31. http://dx.doi.org/10.1111/j.1755-2567.2009.01040.x.

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3

Beck, John. "Powerful knowledge, esoteric knowledge, curriculum knowledge." Cambridge Journal of Education 43, no. 2 (June 2013): 177–93. http://dx.doi.org/10.1080/0305764x.2013.767880.

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4

Richard B. Freeman. "Knowledge, Knowledge… Knowledge for My Economy." KDI Journal of Economic Policy 37, no. 2 (May 2015): 1–21. http://dx.doi.org/10.23895/kdijep.2015.37.2.1.

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5

Sukardi, Sukardi. "Knowledge Discovery Maintaining Intra Industrial Knowledge Flow." International Journal of Psychosocial Rehabilitation 24, no. 1 (January 20, 2020): 1230–40. http://dx.doi.org/10.37200/ijpr/v24i1/pr200223.

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6

Borges. "KNOWLEDGE FROM KNOWLEDGE." American Philosophical Quarterly 57, no. 3 (2020): 283. http://dx.doi.org/10.2307/48574439.

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7

Blackmer, Bruce E. "Knowledge on Knowledge." Journal of Interior Design 31, no. 1 (September 2005): vii—xii. http://dx.doi.org/10.1111/j.1939-1668.2006.tb00411.x.

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8

Enns, Charis. "Knowledges in competition: Knowledge discourse at the World Bank during the Knowledge for Development era." Global Social Policy: An Interdisciplinary Journal of Public Policy and Social Development 15, no. 1 (January 8, 2014): 61–80. http://dx.doi.org/10.1177/1468018113516968.

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9

Kaspar, David. "Moral Knowledge Without Knowledge of Moral Knowledge." Journal of Ethics 26, no. 1 (November 7, 2021): 155–72. http://dx.doi.org/10.1007/s10892-021-09384-0.

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10

Ottinger, Gwen. "Changing Knowledge, Local Knowledge, and Knowledge Gaps." Science, Technology, & Human Values 38, no. 2 (January 2, 2013): 250–70. http://dx.doi.org/10.1177/0162243912469669.

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11

Laperche, Blandine. "Knowledge Management, Knowledge Capital and Knowledge Capitalism." Journal of Innovation Economics & Management N° 43, no. 1 (January 19, 2024): 319–25. http://dx.doi.org/10.3917/jie.043.0319.

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12

Hámornik, Balázs Péter, and Márta Juhász. "Knowledge sharing in medical team: knowledge, knowledge management, and team knowledge." Periodica Polytechnica Social and Management Sciences 18, no. 2 (2010): 99. http://dx.doi.org/10.3311/pp.so.2010-2.05.

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13

Qian, Yuhua, Jiye Liang, and Chuangyin Dang. "Knowledge structure, knowledge granulation and knowledge distance in a knowledge base." International Journal of Approximate Reasoning 50, no. 1 (January 2009): 174–88. http://dx.doi.org/10.1016/j.ijar.2008.08.004.

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14

Wolf, Michael P., and Jeremy Randel Koons. "The Ordinary Language Case for Contextualism and the Relevance of Radical Doubt." Contemporary Pragmatism 15, no. 1 (February 22, 2018): 66–94. http://dx.doi.org/10.1163/18758185-01501005.

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Many contextualist accounts in epistemology appeal to ordinary language and everyday practice as grounds for positing a low-standards knowledge (knowledgeL) that contrasts with high-standards prevalent in epistemology (knowledgeH). We compare these arguments to arguments from the height of “ordinary language” philosophy in the mid 20th century and find that all such arguments face great difficulties. We find a powerful argument for the legitimacy and necessity of knowledgeL (but not of knowledgeH). These appeals to practice leave us with reasons to accept knowledgeL in the face of radical doubts raised by skeptics. We conclude by arguing that by relegating knowledgeH to isolated contexts, the contextualist fails to deal with the skeptical challenge head-on. KnowledgeH and knowledgeL represent competing, incompatible intuitions about knowledge, and we must choose between them. A fallibilist conception of knowledge, formed with proper attention to radical doubts, can address the skeptical challenge without illicit appeal to everyday usage.

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15

Dobrovolska, Olena, Ralph Sonntag, Susan Buschendorf, Elena Klimova, and Wolfgang Ortmanns. "Knowledge creation, knowledge impact and knowledge diffusion: how do they connect with higher education?" Knowledge and Performance Management 7, no. 1 (October 31, 2023): 91–103. http://dx.doi.org/10.21511/kpm.07(1).2023.07.

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Knowledge-based economy causes changes in the higher education system: university graduates must have the ability to constantly learn and improve their skills, generate and disseminate new knowledge, form and multiply the knowledge capital of business. This paper aims to investigate a pairwise interconnection between higher education indicators and sets of parameters characterizing knowledge creation, impact, and diffusion. The following higher education indicators are used: expenditure on education, tertiary enrollment, graduates in science and engineering, tertiary inbound mobility, researcher, gross expenditure on R&amp;amp;D, top 3 global corporate R&amp;amp;D investors, top 3QS university ranking. Knowledge creation indicators are patents by origin, PCT patents by origin, utility models by origin, scientific and technical articles, citable documents, H-index. Knowledge impact is characterized through labor productivity growth, new businesses, software spending, ISO 9001 quality certificates, high-tech manufacturing. Knowledge diffusion parameters include intellectual property receipts, production and export complexity, high-tech exports, ICT services exports. The information base of the study is the data of the Global Innovation Index Report from the World Intellectual Property Organization for 40 European countries (selected depending on the availability of statistics) for 2022, research method – Canonical Correlation Analysis. The strongest positive correlation was found between higher education indicators and knowledge creation parameters. The second position takes connection between higher education indicators and knowledge diffusion parameters, the third – between higher education indicators and knowledge impact indicators. Among the higher education indicators, the most significant were gross expenditure on R&amp;amp;D, top 3 global corporate R&amp;amp;D investors, top 3 QS university ranking.

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16

Asst., Prof. Sujata Kulkarni. "Transition from Knowledge Economy to Knowledge Society." International Journal of Advance and Applied Research 4, no. 5 (February 17, 2023): 129–32. https://doi.org/10.5281/zenodo.7740610.

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The phases of Capitalist economy and Industrial Economy, moving towards waves of Knowledge Economy. The now emerging form of society represents a “knowledge” society because the constitutive mechanism or the identity of modern society increasingly is driven by “knowledge.” Knowledge is an infinite resource. It tends to spread. It works as input as well as output. For centuries, ‘knowledgeable people’ did not work and ‘working people’ were not considered ‘knowledgeable’ or ‘worthy of social respect’. ‘Knowledge’ that was earlier confined to drawing rooms, coffee houses, court-rooms, libraries, and universities, now made waves in shop-floors, factories, places of work, design studios, research and development facilities and corporate offices. In the last few years, importance of knowledge is recognized in economic development as it plays catalyst role in making country more dynamic. We need to go beyond the Knowledge Economy to create the Knowledge Society. People would be both producers and consumers of knowledge, just as we currently produce and consume goods and services. Knowledge is essential to our daily life as well as our work life.

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17

Mahyarni, Mahyarni, M. S. Idrus, Fatchur Rohman, and Noermijati Noermijati. "KNOWLEDGE FACTORS OF KNOWLEDGE-SHARING INTENTION AND BEHAVIOR." Asia Pacific Management and Business Application 1, no. 2 (December 30, 2012): 103–15. http://dx.doi.org/10.21776/ub.apmba.2012.001.02.1.

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18

Shuiping Ni, Shuiping Ni, Wendi Wang Shuiping Ni, Mingfu Zhu Wendi Wang, Xinliang Ma Mingfu Zhu, and Yizhe Zhang Xinliang Ma. "Combined Knowledge Distillation Framework: Breaking Down Knowledge Barriers." 電腦學刊 35, no. 4 (August 2024): 109–22. http://dx.doi.org/10.53106/199115992024083504008.

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<p>Knowledge distillation, one of the most prominent methods in model compression, has successfully balanced small model sizes and high performance. However, it has been observed that knowledge distillation predominantly focuses on acquiring knowledge concealed within the dataset and the external knowledge imparted by the teacher. In contrast, self-distillation concerns itself with the utilization of internal network knowledge. Neither approach fails to fully harness the potential of knowledge. Therefore, this paper introduces the combined knowledge c framework that combines knowledge distillation with self-distillation. Within this framework, we introduce multiple shallow classifiers, combined with an attention module, to exploit internal and external knowledge. To enhance the efficiency with which the network utilizes knowledge. Experimental results demonstrate that by comprehensively leveraging network knowledge, distillation effectiveness can be enhanced, resulting in further improvements in network accuracy. Additionally, we applied the framework to lightweight neural networks with group convolution, the framework continues to perform exceptionally well.</p> <p>&nbsp;</p>

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19

Hasanzadeh, Mohammad, Behrooz Rasuli, and Somayeh Jafari. "Knowledge Management in Practice A Critique on the Book "Knowledge Strategy and Knowledge Management"." Critical Studies in Texts & Programs of Human Sciences 22, no. 5 (June 7, 2022): 267–85. https://doi.org/10.30465/CRTLS.2022.38319.2364.

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Many researchers, professionals, and managers believe that knowledge is one of the key assets of organizations and businesses in the current era. In this regard, the term knowledge strategy has been considered in order to clarify the knowledge assets for the organization, which ultimately improves the efficiency and effectiveness of the organization by implementing knowledge management and formulating a knowledge management strategy. The book "Knowledge Strategy and Knowledge Management" seeks to explain two main concepts, namely "Knowledge Strategy" and "Knowledge Management Strategy" in four chapters. This book is one of the first works in the Persian language to focus on the concept of knowledge strategy. Although the book addresses an important issue that is one of the key needs business managers, the distinction between strategic management, knowledge strategy, strategic knowledge management, etc. is not clearly stated. The purpose of this article is to review the book "Knowledge Strategy and Knowledge Management" from different aspects. 

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20

Stark, Andy. "Public Knowledge, Private Knowledge." Antioch Review 48, no. 4 (1990): 439. http://dx.doi.org/10.2307/4612265.

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21

Brower, Bruce W. "Good Knowledge, Bad Knowledge." International Philosophical Quarterly 44, no. 1 (2004): 107–8. http://dx.doi.org/10.5840/ipq200444168.

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22

Ansovini, Daniela, Kelli Babcock, Tanis Franco, Jiyun Alex Jung, Karen Suurtamm, and Alexandra Wong. "Knowledge Lost, Knowledge Gained." KULA: Knowledge Creation, Dissemination, and Preservation Studies 6, no. 3 (July 27, 2022): 1–19. http://dx.doi.org/10.18357/kula.234.

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Migrating archival description from paper-based finding aids to structured online data reconfigures the dynamics of archival representation and interactions. This paper considers the knowledge implications of transferring traditional finding aids to Discover Archives, a university-wide implementation of Access to Memory (AtoM) at the University of Toronto. The migration and translation of varied descriptive practices to conform to a single system that is accessible to anyone, anywhere, effectively shifts both where and how users interface with archives and their material. This paper reflects on how different sets of knowledge are reorganized in these shifts. Discover Archives empowers researchers to do independent searches using the full breadth of their domain expertise, seemingly unbound from archival gatekeeping. At the same time, these searches are performed in the absence of archivists' unstructured mediation, where searches benefit from human interaction and the kinds of knowledges that reference staff draw on to handle complex reference questions, especially those from novice archival users. We explore the extent to which that lost knowledge can be drawn back into archival interactions via rich metadata that documents contexts and relationships embedded within Discover Archives and beyond. Internal user experience design (UXD) research on Discover Archives highlights a gap between current online description and habitual user expectations in web search and discovery. To help bridge this gap, we contributed to broader discovery nodes such as linked open "context hubs" like Wikipedia and Wikidata, which can supplement hierarchical description with linked metadata and visualization capabilities. These can reintroduce rhizomatic and serendipitous connections, enabled by archivist, researcher, and larger sets of community knowledges, to the benefit of both the user and the archivist.

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23

McNely, Brian J. "Knowledge work, knowledge play." Communication Design Quarterly 2, no. 4 (January 13, 2015): 14–51. http://dx.doi.org/10.1145/2721874.2721876.

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24

Decock, Lieven, Igor Douven, Christoph Kelp, and Sylvia Wenmackers. "Knowledge and Approximate Knowledge." Erkenntnis 79, S6 (November 1, 2013): 1129–50. http://dx.doi.org/10.1007/s10670-013-9544-2.

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25

Stival, Stefano Domingues. "Knowledge and Self-Knowledge." Perspectiva Filosófica 50, no. 3 (February 15, 2024): 386–93. http://dx.doi.org/10.51359/2357-9986.2023.260719.

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This text will be generated by contraposition (in the broadest sense) of two concepts, which I will call “externalist conception of knowledge”, on one side, and “internalist conception of knowledge” (as a kind of psychologist evidentialism), on the other. Through this contrast, I try to reach some new insights into the relationship between the notions of “knowledge” and “self-knowledge”.

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26

Hyttinen, Laura. "Knowledge conversions in knowledge creation in knowledge work." International Journal of Learning and Intellectual Capital 2, no. 3 (2005): 246. http://dx.doi.org/10.1504/ijlic.2005.007098.

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27

Lawton, Jennifer S. "“Knowledge is not knowledge unless it is knowledge”." Journal of Thoracic and Cardiovascular Surgery 155, no. 6 (June 2018): 2539–40. http://dx.doi.org/10.1016/j.jtcvs.2018.02.044.

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28

ZELENY, MILAN. "KNOWLEDGE OF ENTERPRISE: KNOWLEDGE MANAGEMENT OR KNOWLEDGE TECHNOLOGY?" International Journal of Information Technology & Decision Making 01, no. 02 (June 2002): 181–207. http://dx.doi.org/10.1142/s021962200200021x.

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Knowledge Technology (KT) is an important new development, extending and ultimately replacing IT. Meaningful and substantial Knowledge Management (KM) is crucially dependent on a useful and operational definition of knowledge. Such notion of knowledge must be clearly differentiated from so called "explicit (or codified) knowledge", i.e. from information. Information, in any form or shape, is not knowledge. While information is a symbolic description of action, knowledge is action itself. The understanding that knowing is doing and doing is knowing comes from the Western philosophical tradition of pragmatism, exemplified by Dewey, Lewis and Polanyi. In this paper, we look at knowledge as a manifest ability of purposeful coordination of action and redefine the purpose of knowledge management as turning information (description) into knowledge (action) and not vice versa. While there can be an information overload, there is never any "knowledge overload".

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29

JEBASTA, D. SHEELA, and Dr R. BHAVANI Dr.R.BHAVANI. "Knowledge Management." Indian Journal of Applied Research 4, no. 4 (October 1, 2011): 87–89. http://dx.doi.org/10.15373/2249555x/apr2014/233.

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30

Brentjes, Sonja. "Sanctioning Knowledge." Al-Qanṭara 35, no. 1 (June 30, 2014): 277–309. http://dx.doi.org/10.3989/alqantara.2014.012.

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31

ADALARASU, SUBASH K, DR B. "Knowledge Management." SIJ Transactions on Industrial, Financial & Business Management 7, no. 6 (November 29, 2019): 01–06. http://dx.doi.org/10.9756/sijifbm/v7i6/ifbm19045.

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32

Tichá, I., and J. Havlíček. "Knowledge transfer." Agricultural Economics (Zemědělská ekonomika) 53, No. 12 (January 7, 2008): 539–44. http://dx.doi.org/10.17221/1223-agricecon.

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The paper deals with the issue of knowledge transfer from several angles (context, benefits, and channels) in order to specify roles the universities should play in order to enhance the competitiveness of the EU economy. Knowledge transfer encompasses a wide variety of activities that range from appearances in the media and at public forums to participation in bilateral projects, the commercial development of research, the application of expertise through partnerships and internships, and the inclusion of broader community influences in the curriculum to enhance the capabilities of graduates. The broader context to facilitate knowledge transfer activities is assessed, suitable channels identified and some hints to select the appropriate channels provided. As the benefits of knowledge transfer go beyond the simple financial return, factors supporting effective deployment of knowledge transfer function at universities include not only financial incentives, but also a combination of measures ranging from training knowledge transfer personnel, setting appropriate metrics to assess the performance of knowledge transfer processes, quality assurance schemes as well as barriers-removing policies to enhance mobility of staff and free exchange of knowledge.

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33

Choy, S., W. Lee, and C. Cheung. "A Systematic Approach for Knowledge Audit Analysis: Integration of Knowledge Inventory, Mapping and Knowledge Flow Analysis." JUCS - Journal of Universal Computer Science 10, no. (6) (June 28, 2004): 674–82. https://doi.org/10.3217/jucs-010-06-0674.

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Knowledge audit lays a concrete foundation for any knowledge management programs. The central topic of this paper is to integrate various knowledge audit related techniques into pre-audit preparation, in-audit process and post-audit analysis in a systematic manner. Culture assessment, in the form of surveys and radar charts, along with orientation program make up the pre-audit preparation. Structured interviews are carried out to capture process-critical knowledge. Knowledge inventory, knowledge maps and knowledge flow analysis compose of post-audit analysis. Knowledge inventory is then built for stocktaking knowledge assets and thus revealing the key knowledge assets by measuring them against four performance criteria. Knowledge mapping together with social network analysis are to show the knowledge exchange path and make the key knowledge suppliers and customers visible. They are then being further applied into knowledge flow analysis, which serves to reveal the strength and weakness of the current knowledge flow. A case study of applying the designed instruments in the Engineering Division of the Hong Kong Dragon Airlines Limited and the related analysis are also present in this paper.

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34

Muke, Kitoga, Balagizi Ganywamulume, Mambo Mwilo, Tumsifu Manegabe, Masumbuko Bruno, and Birindwa Archippe. "Knowledge Attitude and Practice (KAP) survey among healthcare professionals in pediatrics on the psychomotor development of children. Case of urban and rural health areas in South Kivu in the Democratic Republic of Congo: a cross-sectional study." Annals of Medicine & Surgery 85, no. 8 (July 6, 2023): 3863–69. http://dx.doi.org/10.1097/ms9.0000000000001043.

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Introduction: Development refers to change. The study of development is the understanding of how a subject functions at a particular age or time in life. The objective of this study was to evaluate the knowledge, attitude, and practice of pediatric healthcare personnel on the psychomotor development of the child. Methods: This was a cross-sectional study of knowledge, attitude, and practice conducted in the health areas of South Kivu. Results: Twenty-six caregivers participated in the survey. Men were the most represented (61.5%), mean age 27.62±5.07 years; median experience in pediatrics: 1 year; predominantly urban setting with 69.2%. General practitioners were more represented, with 57.7%. The overall state of knowledge was insufficient in 69.2%. Knowledge1 improves with increasing experience in pediatrics (P=0.008), and qualification improves knowledge1 (P=0.033) and knowledge3 (P=0.009). Knowledge4 improved from rural to urban settings (P=0.022). A good knowledge of the scales used in the evaluation of psychomotor development influences the use of one or the other (P=0.000). The authors also notice that those who indicate and/or have physical therapy practiced in rehabilitation are generally those who have a good knowledge of the use of psychomotor development assessment tools (P=0.010). Conclusion: This study highlights the low theoretical knowledge of caregivers on psychomotor development in our study setting, resulting in late diagnosis and consequently poor treatment of psychomotor development disorders. The results of this study indicate a need for continuing education for pediatric caregivers on the psychomotor development of the child.

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35

Reinhardt, Rüdiger, and Beate Stattkus. "Fostering Knowledge Communication: Concept And Implementation." JUCS - Journal of Universal Computer Science 8, no. (5) (May 28, 2002): 536–45. https://doi.org/10.3217/jucs-008-05-0536.

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The loss of an employee - voluntarily or involuntarily - represents a great risk of losing information and know how as well as breaks the natural knowledge flow. We developed the Knowledge Transfer Meeting Methodology in order to reduce the "brain drain" through a systematic hand-over. The Knowledge Transfer Meeting consists of five modules that support the retrieval and sharing of knowledge systematically and explicitly. The approach promotes a mentorship or partnership philosophy, motivating the leaving employee to share his or her knowledge and experience with a successor. For the implementation of the Knowledge Transfer Meeting Methodology in the company, we identify and train so-called "facilitators" who lead the participants through the process and hence support and spread the methodology within the company.

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36

Naushad, Ali P. M., and Jafar Iqbal. "Knowledge Management in the Major Knowledge Repositories in India." International Journal of Knowledge Engineering-IACSIT 1, no. 1 (2015): 18–23. http://dx.doi.org/10.7763/ijke.2015.v1.3.

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37

Qhal, Eissa Mohammed Ali. "Big Data and Knowledge Management in Virtual Knowledge Incubators." AMBIENT SCIENCE 9, no. 3 (November 2022): 8–16. http://dx.doi.org/10.21276/ambi.2022.09.3.ga01.

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38

Ritu, Bala. "Mathematics Content Knowledge and Pedagogical Content Knowledge." Education India Journal 2, no. 1 (February 28, 2013): 71–78. https://doi.org/10.5281/zenodo.14384015.

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Teachers’ knowledge is a key factor to students’ achievement. One could question what this teachers’ knowledge comprises of content knowledge or pedagogical content knowledge or both. Whether acquiring Mathematics content knowledge alone adequately prepares prospective Mathematics teachers for teaching mathematics. Specifically, does the mastery in arithmetic, algebra, geometry, trigonometry, calculus etc. prepare future teachers to be highly competent to teach students or just acquiring pedagogical knowledge alone adequately prepares prospective Mathematics teachers for teaching mathematic. In the 1980’s, Lee Shulman and his colleagues coined a new term “pedagogical content knowledge” to answer these questions and categorized teacher’s knowledge in seven categories and this give a start to the new debate which one is more better content knowledge or pedagogical content knowledge. This paper is an attempt to define and elaborate the difference and relationship between these two terms Mathematics content knowledge or Mathematics pedagogical content knowledge to make them more understandable to the Mathematics teachers.

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39

Wang, Chenhao, Yubo Chen, Zhipeng Xue, Yang Zhou, and Jun Zhao. "CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16114–16. http://dx.doi.org/10.1609/aaai.v35i18.18029.

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In this paper, we present CogNet, a knowledge base (KB) dedicated to integrating three types of knowledge: (1) linguistic knowledge from FrameNet, which schematically describes situations, objects and events. (2) world knowledge from YAGO, Freebase, DBpedia and Wikidata, which provides explicit knowledge about specific instances. (3) commonsense knowledge from ConceptNet, which describes implicit general facts. To model these different types of knowledge consistently, we introduce a three-level unified frame-styled representation architecture. To integrate free-form commonsense knowledge with other structured knowledge, we propose a strategy that combines automated labeling and crowdsourced annotation. At present, CogNet integrates 1,000+ semantic frames from linguistic KBs, 20,000,000+ frame instances from world KBs, as well as 90,000+ commonsense assertions from commonsense KBs. All these data can be easily queried and explored on our online platform, and free to download in RDF format for utilization under a CC-BY-SA 4.0 license. The demo and data are available at http://cognet.top/.

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40

Hintikka, Jaakko. "Knowledge Acknowledged: Knowledge of Propositions vs. Knowledge of Objects." Philosophy and Phenomenological Research 56, no. 2 (June 1996): 251. http://dx.doi.org/10.2307/2108519.

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41

Chen, Andrew N. K., Yuhchang Hwang, and T. S. Raghu. "Knowledge Life Cycle, Knowledge Inventory, and Knowledge Acquisition Strategies." Decision Sciences 41, no. 1 (February 2010): 21–47. http://dx.doi.org/10.1111/j.1540-5915.2009.00258.x.

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42

Wipawayangkool, Kamphol, and James T. C. Teng. "Profiling knowledge workers’ knowledge sharing behavior via knowledge internalization." Knowledge Management Research & Practice 17, no. 1 (December 25, 2018): 70–82. http://dx.doi.org/10.1080/14778238.2018.1557798.

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43

Dienes, Zoltán, and Ryan Scott. "Measuring unconscious knowledge: distinguishing structural knowledge and judgment knowledge." Psychological Research Psychologische Forschung 69, no. 5-6 (March 15, 2005): 338–51. http://dx.doi.org/10.1007/s00426-004-0208-3.

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44

Akehurst, Gary, Carlos Rueda‐Armengot, Salvador Vivas López, and Daniel Palacios Marqués. "Ontological supports of knowledge: knowledge creation and analytical knowledge." Management Decision 49, no. 2 (March 8, 2011): 183–94. http://dx.doi.org/10.1108/00251741111109106.

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45

van Aalst, Jan. "Distinguishing knowledge-sharing, knowledge-construction, and knowledge-creation discourses." International Journal of Computer-Supported Collaborative Learning 4, no. 3 (June 20, 2009): 259–87. http://dx.doi.org/10.1007/s11412-009-9069-5.

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46

Alexander, Phill. "KNOWing How to Play: Gamer Knowledges and Knowledge Acquisition." Computers and Composition 44 (June 2017): 1–12. http://dx.doi.org/10.1016/j.compcom.2017.03.004.

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47

Varnasari, Ahmadreza, Hassan Mahnaz Tayef, and Beheshti Moluksadat Hosseini. "A Knowledge Map of Knowledge Engineering Scientific Products." Knowledge Processing Studies 2, no. 1 (June 7, 2021): 55–73. https://doi.org/10.5281/zenodo.14040954.

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Drawing a knowledge map of scientific productivity in the field of knowledge engineering in the 2011-2021 period on Web of Science.&lrm; Method: This was an applied descriptive analytical study using a quantitative research design.&lrm; The research population consisted of all scientific products in the field of knowledge engineering, which included 7724 documents published in indexed journals on the Web of Science database in the 2011-2021 period.&lrm; The data were analyzed in Excel.&lrm; VOS viewer was used for constructing the bibliometric networks of researchers, institutions, and countries, Histcite was used for obtaining information through scientometrics methods, and Gephi was used for obtaining betweenness centrality, degree centrality, and eigenvector centrality.&lrm; Findings: Xu, Yang from Beijing University of Science and Technology had the most collaboration with other researchers by publishing 69 documents in the field of knowledge engineering.&lrm; Two Chinese universities were ranked in the first and second place and two Iranian universities were in the third and fourth place.&lrm; Studies on Knowledge Engineering began in the US, the UK, Japan, France, and Australia in the 2010-2012 period.&lrm; Meanwhile, publications in this field have been pursued with more intensity by China, Iran, Spain, and Russia since 2014.&lrm; Results: The findings indicate that many researchers are working in the field of knowledge engineering, with the Chinese researchers being the most active compared to other countries.&lrm; Meanwhile, Asian countries seem to be more involved in this field.&lrm; Furthermore, most of the journals of knowledge engineering were Conference journals.&lrm;

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48

Jordan, Sue. "Medication - Knowledge SetMedication - Knowledge Set." Nursing Standard 21, no. 39 (June 6, 2007): 30. http://dx.doi.org/10.7748/ns2007.06.21.39.30.b626.

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49

Caria, Telmo H. "Knowledge hierarchy and professional knowledge." Cadernos de Pesquisa 44, no. 154 (December 2014): 798–826. http://dx.doi.org/10.1590/198053141992.

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Abstract:

The aim of this article is to substantiate, in the sociological point of view, the distinction between the social and cognitive processes that produce knowledge in knowledge abstract systems - KAS - to generate cultural inequality and the micro processes of knowledge usage, which build local and cultural knowledge from common sense. It is circumscribed to this aim a problematization of knowledge usage developed by middle class salaried professional groups, rich in cultural capital but without equivalent symbolical capital, in a capitalist society at risk. In order to achieve this goal, the classical contributions of Pierre Bourdieu, Boaventura Sousa Santos, Donald Schön and Basil Bernstein (among others) are taken as a basis regarding the limitation of critical and reflexive thinking and the virtues of professional knowledge to support an epistemology of professional practice.

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50

Boland, R. J., J. Singh, P. Salipante, J. D. Aram, S. Y. Fay, and P. Kanawattanachai. "KNOWLEDGE REPRESENTATIONS AND KNOWLEDGE TRANSFER." Academy of Management Journal 44, no. 2 (April 1, 2001): 393–417. http://dx.doi.org/10.2307/3069463.

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