As part of our series on academic freedom, Dylan Kerrigan discusses the wider implications of the financialisation of academic knowledge production by considering academic book publishing. He asks if the success of academic books is best measured by economic or non-economic criteria, by its impact on the business sector or its veracity, by ideological myth-making or evidence.
A culture of bad science can evolve as a result of institutional incentives that prioritize simple quantitative metrics as measures of success, argues Paul Smaldino. But, he adds, not all is lost as new initiatives such as open data and replication are making a positive difference.
Novel breakthroughs in research can have a dramatic impact on scientific discovery but face some distinct disadvantages in getting wider recognition and are often cited as a plus in getting published. But new findings suggest an inherent bias in bibliometric measures against novel research.
Recent findings suggest interdisciplinary research is less likely to be funded than discipline-based research proposals, reports Gabriele Bammer, who argues different review processes may well be required to do justice to these different kinds of interdisciplinarity.
Many academics still operate under the flawed logic that good writing must be complex writing (or vice versa).
The more brazen the willingness to commit academic fraud, the harder it becomes to prevent, suggests Ian Freckelton. So while there is a role for codes of conduct or even criminal courts, finding ways to push temptation to deceive even further out of mind will likeley prove even more successful.
In the past few years there has been an insidious rise in predatory journals and publishers, notes Adele Thomas, and African academics have not been immune to their predation.
In a joint statement, 10 editors representing some of the academia’s most prestigious journals for management, organisational behavior and work psychology research, have vowed to publish research that fails to prove a hypotheses.
An article in the journal Psychological Science, “The New Statistics: Why and How” by La Trobe University’s Geoff Cumming, has proved remarkably popular in the years since and is the third-most cited paper published in a Sage journal in 2013. The idea of an autonomous vehicle – i.e., a self-driving car – isn’t particularly new. Leonardo da Vinci had some ideas he […] Social sciences can also inform the design and creation of ethical frameworks and guidelines for AI development and for deployment into systems. Social scientists can contribute expertise: on data quality, equity, and reliability; on how bias manifests in AI algorithms and decision-making processes; on how AI technologies impact marginalized communities and exacerbate existing inequities; and on topics such as fairness, transparency, privacy, and accountability. If schools provide the proper support and resources, they will help educators move from anxiety to empowerment when integrating AI into the classroom. An article in the journal Psychological Science, “The New Statistics: Why and How” by La Trobe University’s Geoff Cumming, has proved remarkably popular in the years since and is the third-most cited paper published in a Sage journal in 2013. By carefully interrogating the system of economic incentives underlying innovations and how technologies are monetized in practice, we can generate a better understanding of the risks, both economic and technological, nurtured by a market’s structure.
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Impact
Paper Opening Science to the New Statistics Proves Its Import a Decade Later
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A Milestone Dataset on the Road to Self-Driving Cars Proves Highly Popular
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Why Social Science? Because It Can Help Contribute to AI That Benefits Society
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Innovation
AI Upskilling Can and Should Empower Business School Faculty
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Paper Opening Science to the New Statistics Proves Its Import a Decade Later
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To Better Forecast AI, We Need to Learn Where Its Money Is Pointing
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