{"id":5171,"date":"2022-12-20T18:25:45","date_gmt":"2022-12-20T18:25:45","guid":{"rendered":"https:\/\/science-metrix.com\/?p=5171"},"modified":"2022-12-20T18:25:51","modified_gmt":"2022-12-20T18:25:51","slug":"analyzing-ref-outputs","status":"publish","type":"post","link":"https:\/\/science-metrix.com\/fr\/analyzing-ref-outputs\/","title":{"rendered":"Analyse des r\u00e9sultats issus du cadre d\u2019excellence en recherche (REF) au Royaume-Uni"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"5171\" class=\"elementor elementor-5171\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-576e5d85 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"576e5d85\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-97de221\" data-id=\"97de221\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-35cce956 elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-spacer\" data-id=\"35cce956\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-33eaf026 elementor-widget elementor-widget-heading\" data-id=\"33eaf026\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Analyzing REF outputs: exploring new approaches<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-453c788 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"453c788\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ce56d42\" data-id=\"ce56d42\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-776518ee elementor-widget elementor-widget-text-editor\" data-id=\"776518ee\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>December 2022<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-24229ef3 elementor-reverse-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"24229ef3\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-5575532e\" data-id=\"5575532e\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-45ecf3ba elementor-widget elementor-widget-text-editor\" data-id=\"45ecf3ba\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"font-weight: 400;\">REF\u2014the Research Excellence Framework\u2014is an evaluation of the research impact of UK higher education institutions. In a recent study, Science-Metrix, collaborating with Technopolis, investigated the possibilities for using machine learning and automated processes on\u00a0<a href=\"https:\/\/www.ref.ac.uk\/about-the-ref\/what-is-the-ref\/\" target=\"_blank\" rel=\"noopener\">REF 2021<\/a>\u00a0data. The goal of this study, which was commissioned by a group of UK funders, was to deepen understanding of the UK research landscape within disciplines, sub-disciplines and research areas. The REF 2021 used Units of Assessment (UoA) to categorize outputs submitted by UK institutions; this study explored alternatives to that system, using a data-driven and automated approach that would allow more granularity or flexible categorizations that cut across UoA. Through several sub-projects, Science-Metrix tested different methodologies and explored their benefits and limitations.<\/p><p style=\"font-weight: 400;\">Several recommendations came out of the study regarding classification approaches. In a sub-project involving medical research areas, Science-Metrix experimented with both bottom-up and top-down classifications, concluding that in the medical sub-disciplines, a top-down approach using machine learning (ML) provided a reliable classification of REF outputs that paralleled the categorizations of those outputs as submitted to the UoA. In contrast, bottom-up approaches provided a classification that was too granular to be meaningful for the REF exercise but could serve to identify emerging research areas.<\/p><p style=\"font-weight: 400;\">In a second area of investigation, Science-Metrix took an experimental approach to building a thematic data set on the cross-cutting theme of \u201caging and gerontology\u201d (a theme which was not part of the REF UoA classification, but which is highly relevant to one of the UK\u2019s Grand Challenges). Publications on this theme were identified using a query-based approach and covered all scientific disciplines. The thematic query was then extended to incorporate not only traditional research outputs such as peer-reviewed journal publications, but also non-traditional outputs (for example, films or books).The use of the bottom-up approach on full-text (i.e., title, abstract, author, author keywords and reference) created unreliable results, because it included the text of references, which were not necessarily relevant to the topic, and author names, which might coincidentally match search terms. The study recommended structuring the full text to identify and search only certain sections (keywords, title, abstract) to reduce false positive results.<\/p><p style=\"font-weight: 400;\">Similarly, in a sub-project on interdisciplinarity, a broader approach to measure disciplinary diversity of research was tested, again through the ML algorithm, with the goal of producing a new metric. More work will be needed to refine the use of this metrics on full-text, particularly for non-traditional research outputs.<\/p><p style=\"font-weight: 400;\">In another sub-project on interdisciplinarity, an attempt was made to\u00a0develop a new ML method to capture the degree of knowledge integration, from diverse disciplines (interdisciplinarity), using the full text of submitted REF outputs. Although interdisciplinary indicators capturing the disciplinary diversity of individual scientific publications using information on references and authors already exist, these indicators are not, contrary to a method built on full-text, directly applicable to non-traditional outputs. The results based on the new ML-based indicators were not conclusive at this stage, but Science-Metrix alternatives (using references and authors) worked out well to mitigate the risk and provided insightful results on interdisciplinary patterns in the UK. Further work is needed to improve the ML-based approach.<\/p><p style=\"font-weight: 400;\">Overall, the study concluded that caution is warranted when automatic methods are used in assessment. Although automatic processes can be applied, substantial work is still required in prepping the data and automated methods \u201cdo not replace the need for thematic expertise and peer-reviewed assessment.\u201d Expert guidance on thematic areas, for example, is still essential to shape classification methodologies and ensure meaningful results.<\/p><p><a style=\"background-color: #ffffff; letter-spacing: 0px;\" href=\"https:\/\/repository.jisc.ac.uk\/8982\/1\/ref-outputs-maximising-the-use-of-ref-data-main-report.pdf\" target=\"_blank\" rel=\"noopener\">Read the report<\/a><span style=\"background-color: #ffffff; letter-spacing: 0px;\">\u00a0[PDF]<\/span>.<\/p><p>Image credit: iStock<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-4d38f47c\" data-id=\"4d38f47c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-44d07c37 elementor-widget elementor-widget-image\" data-id=\"44d07c37\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"800\" height=\"534\" src=\"https:\/\/www.science-metrix.com\/wp-content\/uploads\/2022\/12\/iStock-1212102699-1024x683.jpg\" class=\"attachment-large size-large wp-image-5184\" alt=\"gears hanging in the air above an open laptop\" srcset=\"https:\/\/www.science-metrix.com\/wp-content\/uploads\/2022\/12\/iStock-1212102699-1024x683.jpg 1024w, https:\/\/www.science-metrix.com\/wp-content\/uploads\/2022\/12\/iStock-1212102699-300x200.jpg 300w, https:\/\/www.science-metrix.com\/wp-content\/uploads\/2022\/12\/iStock-1212102699-768x512.jpg 768w, https:\/\/www.science-metrix.com\/wp-content\/uploads\/2022\/12\/iStock-1212102699-18x12.jpg 18w, https:\/\/www.science-metrix.com\/wp-content\/uploads\/2022\/12\/iStock-1212102699.jpg 1254w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Analyzing REF outputs: exploring new approaches December 2022 REF\u2014the Research Excellence Framework\u2014is an evaluation of the research impact of UK higher education institutions. In a recent study, Science-Metrix, collaborating with Technopolis, investigated the possibilities for using machine learning and automated processes on&nbsp;REF 2021&nbsp;data. The goal of this study, which was commissioned by a group of [&hellip;]<\/p>\n","protected":false},"author":11,"featured_media":5184,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[11],"tags":[14,41,64,79,77,80,78],"class_list":["post-5171","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-bibliometrics","tag-classification","tag-interdisciplinarity","tag-machine-learning","tag-ref","tag-thematic-data-sets","tag-uk"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Analyzing REF outputs: exploring new approaches - Science-Metrix<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.science-metrix.com\/fr\/analyzing-ref-outputs\/\" \/>\n<meta property=\"og:locale\" content=\"fr_CA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Analyzing REF outputs: exploring new approaches - Science-Metrix\" \/>\n<meta property=\"og:description\" content=\"Analyzing REF outputs: exploring new approaches December 2022 REF\u2014the Research Excellence Framework\u2014is an evaluation of the research impact of UK higher education institutions. In a recent study, Science-Metrix, collaborating with Technopolis, investigated the possibilities for using machine learning and automated processes on&nbsp;REF 2021&nbsp;data. 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