With research-based evidence increasingly being seen in policy, we should acknowledge that there are risks that the research or ‘evidence’ used isn’t suitable or can be accidentally misused for a variety of reasons.
Inspired by ‘design sprints’ a Google where projects could create a prototype in five days, the authors started doing ‘research sprints’ in 2015.
This article by Bent Flyvbjerg examines the misconceptions and strategic misrepresentations that routinely result in the implementation of projects for which there is inadequate justification, absorbing funds that could have been better spent elsewhere.
Over a 10-year period Carol Tenopir of DataONE and her team conducted a global survey of scientists, managers and government workers involved in broad environmental science activities about their willingness to share data and their opinion of the resources available to do so (Tenopir et al., 2011, 2015, 2018, 2020). Comparing the responses over that time shows a general increase in the willingness to share data (and thus engage in Open Science).
The double-blind review process, adopted by many publishers and funding agencies, plays a vital role in maintaining fairness and unbiasedness by concealing the identities of authors and reviewers. However, in the era of artificial intelligence (AI) and big data, a pressing question arises: can an author’s identity be deduced even from an anonymized paper (in cases where the authors do not advertise their submitted article on social media)?
Recent experiences have not been very positive. The vast majority of proposals seem to conflate impact with research dissemination (a heroic leap of faith – changing the world one seminar at a time), or to outsource impact to partners such as NGOs and thinktanks.
The Covid-19 pandemic seems to be subsiding into a low-level endemic respiratory infection – although the associated pandemics of fear and action […]
The claim that academics hype their research is not news. The use of subjective or emotive words that glamorize, publicize, embellish or exaggerate results and promote the merits of studies has been noted for some time and has drawn criticism from researchers themselves. Some argue hyping practices have reached a level where objectivity has been replaced by sensationalism and manufactured excitement. By exaggerating the importance of findings, writers are seen to undermine the impartiality of science, fuel skepticism and alienate readers.
Drawing on discussions with academics who have oriented their work around public engagement and social impact, Daniel Pearson suggests these academics present an opportunity to rethink the existing structures of reward and recognition in higher education. “We are, as a species, addicted to story,” says English professor Jonathan Gottschall in his book, The Storytelling Animal. “Even when the […] Tom Burns, whose combination of play — and plays – with teaching in higher education added a light, collaborative and engaging model […] A 2024 report by the National Academies explores the latest advances in artificial intelligence (AI) technology and their potential effects on economic productivity, job stability, and income inequality. It also highlights key research opportunities and data needs to help workers and policymakers adapt to the evolving AI landscape. In this post, co-authors Frank T. Piller, Tucker J. Marion, and Mahdi Srour reflect on the inspiration behind their research article, “Generative […] During the final stages of editing the proofs for Artificial Intelligence and Work: Transforming Work, Organizations, and Society in an Age of Insecurity, […]
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Impact
Young Scholars Can’t Take the Field in Game of Academic Metrics
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Canada’s Storytellers Challenge Seeks Compelling Narratives About Student Research
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Tom Burns, 1959-2024: A Pioneer in Learning Development
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Innovation
NAS Report Examines Nexus of AI and Workplace
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When Do You Need to Trust a GenAI’s Input to Your Innovation Process?
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The Authors of ‘Artificial Intelligence and Work’ on Future Risk
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