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Academics share machine-learning research freely. Taxpayers should not have to pay twice to read our findings
[...] In my own field of machine learning, itself an academic descendant of Gauss’s pioneering work, modern data are no longer just planetary observations but medical images, spoken language, internet documents and more. The results are medical diagnoses, recommender systems, and whether driverless cars see stop signs or not. Machine learning is the field that underpins the current revolution in artificial intelligence.
Machine learning is a young and technologically astute field. It does not have the historical traditions of other fields and its academics have seen no need for the closed-access publishing model. The community itself created, collated, and reviewed the research it carried out. We used the internet to create new journals that were freely available and made no charge to authors. The era of subscriptions and leatherbound volumes seemed to be behind us.
The public already pays taxes that fund our research. Why should people have to pay again to read the results? Colleagues in less well-funded universities also benefit. Makerere University in Kampala, Uganda, has as much access to the leading machine-learning research as Harvard or MIT. The ability to pay no longer determines the ability to play.
Machine learning has demonstrated that an academic field can not only survive, but thrive, without the involvement of commercial publishers. But this has not stopped traditional publishers from entering the market. Our success has caught their attention. Most recently, the publishing conglomerate Springer Nature announced a new journal targeted at the community called Nature Machine Intelligence. The publisher now has 53 journals that bear the Nature name.
[...] at the time of writing, more than 3,000 researchers, including many leading names in the field from both industry and academia, have signed a statement refusing to submit, review or edit for this new journal. We see no role for closed access or author-fee publication in the future of machine-learning research.
(Score: 1) by unauthorized on Friday June 01 2018, @12:13AM (1 child)
I think I see the point of disagreement clearly now, I myself tend see journals as a holistic institution (ie yhe reviewers are ARE the journal), and you only see them as the discrete publishing entity. Or to put it in other words, it's like the difference of seeing a nation as it's citizenry, rather than purely as the state itself.
(Score: 2) by AthanasiusKircher on Friday June 01 2018, @04:04AM
Okay, I understand that perspective. But note the link I gave in my first reply to you: it was about editorial boards leaving extant journals en masse and forming new journals often with better access and less unnecessary corporate bureaucracy.
That's what I'm advocating for... Effectively preserving extant scholarly communities but migrating the people to better platforms. I don't see the point in continuing to support existing corporate journal infrastructure if they are not responsive to the demands of the academics who run them.