Weekend reads: Idiotic reviews; wrong metrics in China; questions about preprints

booksThe week at Retraction Watch featured the corrections of papers claiming that conservative beliefs were linked to psychotic traits, and a new member of our leaderboard, from philosophy. Here’s what was happening elsewhere:

Like Retraction Watch? Consider making a tax-deductible contribution to support our growth. You can also follow us on Twitter, like us on Facebook, add us to your RSS reader, sign up on our homepage for an email every time there’s a new post, or subscribe to our new daily digest. Click here to review our Comments Policy. For a sneak peek at what we’re working on, click here.

4 thoughts on “Weekend reads: Idiotic reviews; wrong metrics in China; questions about preprints”

  1. The article “Why are so many researchers moving to Qatar?” can be recommended as a good example of contemporary Orwellian newspeak. Interesting that two female authors would compile an article that fades out obvious critical questions. Also in the news today: “Dutch woman jailed for reporting her rape in Qatar”.
    If you can read between the lines, you will note that a great things about Qatar is that “Qatar airlines fly everywhere. When we have the opportunity, we just go somewhere; my family loves it…” or “Qatar is a fantastic place to live because it is so central … we can take our kids to Europe, Asia, and Africa to see the world”. So the best thing about the place seems to be that you can easily get away… (unless you’ve already been arrested like the Dutch woman).

  2. I wonder if you might include in the next weekend reads the upcoming workshop, HORSE 2016 (http://c4dm.eecs.qmul.ac.uk/horse2016). A “horse” is a machine learning system promoted as solving a specific problem but that is actually solving an unrelated problem. Poor experimental methodology and unacknowledged assumptions and limitations are to blame. My research has uncovered several published “horses”, but retractions are not typical in my field (machine listening). HORSE 2016 is devoted to discussing “horses”, and promoting superior research methods in applied machine learning. So far, I have contributions from computational creativity, surveillence, finance, and reproducible research efforts. Thank you!

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.