Why Thousands of AI Researchers Are Boycotting the New Nature Journal
Published on May 29, 2018 at 11:40PM
An anonymous reader shares an excerpt from a report via The Guardian, written by Neil Lawrence, the founding editor of the freely available journal Proceedings of Machine Learning Research: 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. Should we be concerned? What would drive authors and readers towards a for-profit subscription journal when we already have an open model for sharing our ideas? Academic publishers have one card left to play: their brand. The diversity and quantity of academic research means that it is difficult for a researcher in one field to rate the work in another. Sometimes a journal’s brand is used as a proxy for quality. When academics look for promotion, having papers in a “brand-name journal” can be a big help. Nature is the Rolex of academic publishing. But in contrast to Rolex, whose staff are responsible for the innovation in its watches, Nature relies on academics to provide its content. We are the watchmakers, they are merely the distributors.
Many in our research community see the Nature brand as a poor proxy for academic quality. We resist the intrusion of for-profit publishing into our field. As a result, 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. We believe the adoption of this new journal as an outlet of record for the machine-learning community would be a retrograde step.
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