Concerns That 10,000 Faculty Nationally and Internationally Have About Research Impact: Isn’t It All Academic Research Impact (ARI) If It Comes From The Academy?
Notice bibliographique
Résumé
The Paradigmatic Shift Towards Research Impact in the AcademyIn the last four decades there has been increased emphasis for faculty to show and effectively expand the impact of their Academic Research Work*. This trend has occurred nationally, internationally, and is expected to persist through recent national and international events. There are now summits and conferences such as the National Alliance for Broader Impacts Summit (NABI) now called Advancing Research in Society (ARIS) in the United States (US) and International Impact for Science, Humanities, and Social Science Conferences. Many of these international conferences that focus on pushing forward the impact agenda are facilitated by The Network for Advancing & Evaluating the Societal Impact of Science (AESIS).There is rapid proliferation of both new businesses and independent organizations that focus on helping others manage and maximize the impact of their research. These businesses and organizations range from assessment to communications and scholarship, to training individuals on how to extend the reach of their research work. Some examples of these are: Knowledge Translation Australia by Tamika Heiden who also facilitates the largest online impact summit; Jenny Ames Consulting Ltd in the United Kingdom (UK) by Jenny Ames; Institute for Knowledge Mobilization and Peter Norman Levesque Consulting in Canada by Peter Levesque; and Broader Impacts Productions, LLC in the United States (US) by Kirsten Sanford.Simultaneously, impact blogs and blogging have increased in number over the last ten years. These impact blogs are also increasingly gaining support and recognition in the Academy. For example, one of these is the London School of Economic and Political Science (LSE) Impact Blog. The LSE Impact blog is based in the LSE Communications division and is financially supported by the HEIF5 program ran by LSE Knowledge Exchange.Evidence of this paradigmatic shift can also be seen by the number of societal benefitting-like terms, names and phrases now being used around the world. Many of these terms, names, and phrases have been contextualized for maximizing the impact of academic research. These include but are not limited to phrases, terms, and concepts such as: Capacity Building in Africa; Equity in Development in India; Broader Impacts, Broader Implications, Collective Impact, and Relevant or Ultimate Outcomes in the US; the Engagement and Impact Assessment (EI) Framework and Knowledge Exchange in Australia; Knowledge Mobilization in Canada; Valorization in the Netherlands; Harmonious Development in South America; Economic & Social Development and Influence in China; and the Research Excellence Framework (REF) in the European Union. Almost every country, roughly eighty-two percent (82%), uses a societal-benefit name, term, phrase, or concept that indicates ARI is important. This is accompanied by the growing number of professionals and positions to address and actionize these concepts, names, and phrases in academic institutions, agencies, organizations, and governance. For example, there is Susan Renoe, Director of NABI and ARIS who started The Connector formally called the Broader Impacts Network (BIN) in the US; David Phillips who leads an award-winning Knowledge Mobilization Unit in Canada; Julie Bayley who is the Director of Impact Development and Mark Reed who is a professor and transdisciplinary researcher specializing in environmental governance and research impact in peatlands and agri-food systems both in the UK; and Emma Johnston who initiated a Science for Impact Center in Australia focusing on Knowledge Exchange.In addition, some ranking organizations have started to include Overall Impact on Society (OIS) metrics to rank universities and colleges. This includes how university’s and college’s research are benefiting society. For example, in 2019 “THE WORLD University Rankings” facilitated by Times Higher Education, provided their first ever rankings specifically focused on University’s and College’s Impact on Society. These impact rankings are based on the United Nations (UN) seventeen (17) Sustainable Development Goals (SDGs) provided in Figure 1.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,076 | 0,237 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,008 |
| Études des sciences et des technologies | 0,012 | 0,015 |
| Communication savante | 0,022 | 0,026 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,015 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».