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您的检索式：作者名="Kayvan Kousha"
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| 1 | How is Science Cited on the web--A Classification of Google Unique Web Citations显示文摘 | Mike Thelwall Kayvan Kousha | 2007 | Journal of the American Society for Information Science and Technology2007，58，11： | 1 |
| 2 | Motivations for URL citations to open access library and information science articles显示文摘 | Kayvan Kousha Mike Thelwall | 2006 | Scientometrics2006，，3： | 1 |
| 3 | The Web impact of open access social science research显示文摘 | Kayvan Kousha Mike Thelwall | 2007 | Library & Information Science Research2007，29，4： | 1 |
| 4 | An Automatic Method to Identify Citations to Journals in News Stories: A Case Study of UK Newspapers Citing Web of Science Journals显示文摘Purpose: Communicating scientific results to the public is essential to inspire future researchers and ensure that discoveries are exploited. News stories about research are a key communication pathway for this and have been manually monitored to assess the extent of press coverage of scholarship.Design/methodology/Approach: To make larger scale studies practical, this paper introduces an automatic method to extract citations from newspaper stories to large sets of academic journals. Curated ProQuest queries were used to search for citations to 9,639 Science and3,412 Social Science Web of Science(WoS) journals from eight UK daily newspapers during2006–2015. False matches were automatically filtered out by a new program, with 94% of the remaining stories meaningfully citing research.Findings: Most Science(95%) and Social Science(94%) journals were never cited by these newspapers. Half of the cited Science journals covered medical or health-related topics,whereas 43% of the Social Sciences journals were related to psychiatry or psychology. From the citing news stories, 60% described research extensively and 53% used multiple sources,but few commented on research quality.Research Limitations: The method has only been tested in English and from the ProQuest Newspapers database.Practical implications: Others can use the new method to systematically harvest press coverage of research.Originality/value: An automatic method was introduced and tested to extract citations from newspaper stories to large sets of academic journals. | Kayvan Kousha Mike Thelwall | 2019 | Journal of Data and Information Science2019，4，3： | 1 |
| 5 | Motivations for URL Citations to Open Access Library and Information Science Articles显示文摘 | Kayvan Kousha Mike Thelwall | 2006 | Scien- tometrics2006，68，3： | 1 |
| 6 | Is big team research fair in national research assessments? The case of the UK Research Excellence Framework 2021显示文摘Collaborative research causes problems for research assessments because of the difficulty in fairly crediting its authors.Whilst splitting the rewards for an article amongst its authors has the greatest surface-level fairness,many important evaluations assign full credit to each author,irrespective of team size.The underlying rationales for this are labour reduction and the need to incentivise collaborative work because it is necessary to solve many important societal problems.This article assesses whether full counting changes results compared to fractional counting in the case of the UK’s Research Excellence Framework(REF)2021.For this assessment,fractional counting reduces the number of journal articles to as little as 10%of the full counting value,depending on the Unit of Assessment(UoA).Despite this large difference,allocating an overall grade point average(GPA)based on full counting or fractional counting gives results with a median Pearson correlation within UoAs of 0.98.The largest changes are for Archaeology(r=0.84)and Physics(r=0.88).There is a weak tendency for higher scoring institutions to lose from fractional counting,with the loss being statistically significant in 5 of the 34 UoAs.Thus,whilst the apparent over-weighting of contributions to collaboratively authored outputs does not seem too problematic from a fairness perspective overall,it may be worth examining in the few UoAs in which it makes the most difference. | Mike Thelwall Kayvan Kousha Meiko Makita Mahshid Abdoli Emma Stuart Paul Wilson Jonathan Levitt | 2023 | Journal of Data and Information Science2023，8，1： | 0 |
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