It’s nice to be right sometimes…

I philosophize a lot about the role of journals and scientific publishing as part of the academic ecosystem – and especially how this ecosystem will change. Only last week my boss, Arjun, and I published a blog post (Results from the Guess the Impact Factor Challenge) analyzing how people perceive the of titles of scientific articles. But even back in 2015 I played around with predicting how the role of journals might shift. One of the predictions I made was that

…in The MacGyver age of content creation […], from pre-print servers (like BioRxiv) through science blogging and open lab notebooks, […] “legacy” journals as they exist today will become obsolete. […] Their role will change. So, taking a leaf from the media book, I predict that a key function of journals in the future will be to connect readers to relevant content published across the web.

Now it’s 2017 and Springer Nature just announced Recommended, “a new service which connects the research community with the most relevant content”.

It is definitely nice to be right sometimes 🙂

Looking forward, this also means that we should now be thinking about life after preprints: While in theory preprints could have a truly democratizing effect (anyone anywhere would be able to access papers without a paywall), in practice I’m not sure that potential will be realized. If people can’t trawl online content efficiently, they will rely on recommendation services, which – if in the wrong hands – may well introduce biases and favoritism. Thus, an effort to develop a fair (ideally mainly content/quality-driven) recommendation service hand-in-hand with the open access/preprint movement will be vital to create a healthy, balanced publishing landscape…

 

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7 thoughts on “It’s nice to be right sometimes…

    • True, but the difference is that a well-established publisher should opt for this move. It’s not like we didn’t have our Sunday newspaper TV guide before amazon started making recommendations 😉 Noone wants to go out of their way for information, so i think the winning model will be if a journal can integrate your reading/citing habits to tailor recommendations to you (eg rather than you having to like/dislike articles or read generic recommendations).

      • I’m not sure I agree. I think that journals themselves are essentially incidental and only serve to “rank” work in some way. If I want to find a paper on Hox genes or whatever, it’s not like I look at the journal “Annals of Hox genes”. I just search for Hox genes across all journals. Thus, any recommendation system based on a journal doesn’t make a lot of sense in my opinion, as they will almost certainly favor their own content. Overlay journals make more sense, but I don’t know how those will work in the biomedical field.

      • I like the PubChase model of working with a library of papers that you typically maintain yourself. Only problem is that it doesn’t integrate with e.g. PaperPile.

    • I liked Pubchase at first, but truthfully, I switched back to my pubmed search emails.
      Maybe its b/c my library wasn’t properly updated, or something else; but I got many remotely relevant papers and missed many relevant papers.
      Also, Pubchase requires constant updating my library and it was just too much work.

      Basically, I use Twitter as a primary source for interesting/cool papers (some are directly in my research interests, some are not, but interesting enough to read the abstract and browse through the results) and I browse eTOCs of ~10 journals. I use the pubmed weekly emails for a more specific search.

      I think that what we need is a better search engine (e.g. to include preprints, open data sources, blogs on one hand and to narrow the search so we don’t get 100s of results on the other hand) than a “personalized” recommendations engine.

      I don’t think journals are going to disappear anytime soon anyway. Particularly since the career path of young scientist still heavily depends on where you publish (and also, how much) and not just what you published. Things are starting to change, so I hope that the journal name will be a non-issue for my PhD students or postdocs in 5-10-20 years.
      I think I sidetracked to a different topic.
      Anyway, as Arjun also said, these recommendations will probably be biased to favor their own content.

      • Yes, I think a lot of us currently use a combination of different methods to find interesting/relevant papers. I just wonder how long that will be sustainable? Plus, I’m sure that with today’s AI/data mining tools, we can build better algorithms than the existing ones. For example, Recommend’s idea of tracking your browser (rather than you having to maintain a library) might be a step in that direction. To be honest, I’m not sure how quickly the changes that I predict will happen. Based on the open access movement (founded in the early 2000s and now at the brink of being commonly accepted), I would say 15-20 years, but who knows…

  1. I agree that we currently don’t do this – and I think that’s exactly what has to/will change: just as Netflix recommends shows based on your viewing experience, your recommendation service will recommend papers for you based on your reading experience. And yes, such services already exist, but for traditional publishers to survive I think they will need to shift their business model in that direction, too. I don’t know exactly how this will be implemented, but I think it lacks foresight for the pre-print movement to think the battle will be won, once everybody uses preprints. I think it’s time to focus on what comes after that, ie how will people find content and who will be the major players for recommending content. Theoretically anyone could look at any video content on youtube, but practically most people most of the time just click on their recommendations in Netflix/Amazon…

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