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Record 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 on OpenAlexaffabout
Bruce Alder

Bibliographic record

VenueRenewable Energy and Sustainable Development · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsLoginPasswordScope (computer science)HeaderWorld Wide WebComputer scienceInformation retrievalComputer securityComputer network

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.325
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2016
Admission routes2
Has abstractyes

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