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SUMMARY:Statistical biases in peer review - Remco Heesen (Faculty of Philo
 sophy)
DTSTART:20180426T143000Z
DTEND:20180426T160000Z
UID:TALK99244@talks.cam.ac.uk
CONTACT:Agnes Bolinska
DESCRIPTION:Various biases are known to affect the peer review system\, wh
 ich is used to judge journal articles for their suitability for publicatio
 n and grant proposals for their suitability for funding. These biases are 
 generally attributed to cognitive biases held by individual peer reviewers
 . For example\, gender bias in peer review is explained by the (explicit o
 r implicit) gender bias of individual peer reviewers\, as evidenced by the
  generally lower scores given to submissions authored by women. Here I int
 roduce the notion of 'purely statistical biases': biases in peer review th
 at arise even when individual peer reviewers are unbiased. This notion sug
 gests that certain social groups or research programs may be disadvantaged
  by the peer review system even in the absence of cognitive biases. I use 
 formal models to identify three possible mechanisms for purely statistical
  biases. The first mechanism relies on differences in information about au
 thors available to decision makers. The second mechanism relies on differe
 nces in the underlying distributions of the 'quality' of submissions. Fina
 lly\, the third mechanism comes into play when reviewers judge submissions
  on multiple criteria: aggregating these judgments into a final decision l
 eads to a third possible source of bias.
LOCATION:Seminar Room 2\, Department of History and Philosophy of Science
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