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Enregistrement W2555261367 · doi:10.21622/resd.2016.02.1.002

Page Header OPEN JOURNAL SYSTEMS Journal Help USER Username yasser Password •••••••••• Remember me Login NOTIFICATIONS View Subscribe JOURNAL CONTENT Search Search Scope Search Browse By Issue By Author By Title Other Journals Categories FONT SIZE Make font size smallerMake font size defaultMake font size larger INFORMATION For Readers HOME ABOUT LOGIN REGISTER CATEGORIES SEARCH CURRENT ARCHIVES ANNOUNCEMENTS Home > Archives > Vol 2, No 1 (2016) Vol 2, No 1 (2016) RESD Volume 2, Issue 1, June 2016 Table of Contents Editorials Developing Water Resources Within and Without Borders: Egypt’s Road to Achieve Sustainable Development PDF Hossam Moghazy 1 Silent Revolution in Research for Sustainability

2016· article· en· W2555261367 sur OpenAlexaffabout
Bruce Alder

Notice bibliographique

RevueRenewable Energy and Sustainable Development · 2016
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueTransboundary Water Resource Management
Établissements canadiensInternational Development Research Centre
Organismes subventionnairesnon disponible
Mots-clésLoginPasswordScope (computer science)HeaderWorld Wide WebComputer scienceInformation retrievalComputer securityComputer network

Résumé

récupéré en direct d'OpenAlex

Is research ‘fit-for-purpose’ for realizing sustainable development? More than two decades after the Brundtland report and UNCED Earth summit, the world has now adopted Sustainable Development Goals (SDGs). Rather than a cause for celebration, this delay should encourage reflection on the role of research in society. Why is it so difficult to realize sustainability in practice? The answer lies in the fact that universities and research centres persist with 19th century methods of data gathering, scholarly analysis, and journal articles. Today’s world needs science in real-time, whether to detect drought, confront Ebola, or assist refugees. Research needs to work faster and embrace 21st century practices including data science, open access, and infographics. A silent revolution is occurring in the ways of organizing and conducting research, enabled by new technology and encouraging work that tackles the key challenges facing society. A variety of new arrangements have come into existence that promote international collaboration, including Horizon 2020 with its emphasis on societal challenges, the Bill & Melinda Gates Foundation which has inspired a family of grand challenges funds on health and development, and the Future Earth joint program of research for global sustainability. These arrangements not only control billions of dollars in research funding, they also influence the strategies of national research councils and international organizations. The result is no less than a transformation in the incentives that reward how researchers invest their time and effort. Why is a revolution needed? Within research, substantial growth in knowledge production coincided with fragmentation among disciplines. One can easily find expertise and publications in soil science or agronomy, yet integrated efforts on food security and climate adaptation remain scarce. Beyond research, society remains largely uninformed, as academics avoid engaging in public debate or policy advice. Research often fails to raise public awareness or inform practitioners regarding the issues facing society and the options for responding to them. For example, research on food security can and must go beyond quantifying how many people are hungry or undernourished. Society needs solutions that connect changes in farm-level production, to how the market mediates access to food, and the ultimate health outcomes among citizens. The emerging vision is one where research helps society understand and respond to global problems. Research that is ‘fit-for-purpose’ demonstrates an ability to bridge ingenuity gaps, address grand challenges, and foster social resilience. Ingenuity gaps concern the knowledge needed to address rising complexity and new vulnerabilities introduced by globalization and technological change. Grand challenges describe a shift in the scale, scope, and ambition of research objectives. Social resilience refer to society's ability to cope with stress and reinvent itself in response to shocks and pressures. In short, together these attributes describe an expectation that research helps society to 'mind the gap', 'think big', and 'bounce'. Research needs to speak back to society. While the journal article and scholarly publications remain important determinants of a research career, they are increasingly supplemented by attention to data visualization, social media, and research impact. Research still needs rigour: deep knowledge of theory and data, and how to uncover patterns and establish explanation. Scientists have a long history of using pie charts, line graphs, and network diagrams to communicate among themselves. Yet research also needs a keen sense of design: an appreciation for how to convey relationships, categories, and magnitude through the creative use of lines, colours, symbols, position and size. Evidence-based illustrations, or infographics, convey complex issues in greater depth than long reports or TV commentaries. Research tells a story: starting with a compelling problem or question, and using data to provide perspective. Rather than offer society potential solutions or policy recommendations, newer techniques allow anyone to interact with data to create their own visualizations and test hypotheses “on demand”. In summary, there is a silent revolution in research for sustainability. Research is expected to help understand and address the problems facing society. The opportunities to engage in research are shifting, rewarding those who are embrace the practices of open science and data, those who are connected to international scientific networks, and those that help society to better understand and solve global problems. Bruce Currie-Alder Regional Director, Canada's International Development Research Centre (IDRC) in Cairo

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,023
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Communication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,850
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0230,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0050,001
Communication savante0,0040,002
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,048
Tête enseignante GPT0,325
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission2
Résumé présentoui

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