Swiss funder draws lots to make grant decisions
- CAREER NEWS
Swiss funder draws lots to make grant decisions
Credit: Peter Ekvall/Shutterstock
The Swiss National Science Foundation (SNSF) , Switzerland’s largest government research-funding agency, has started using a random-selection process as a tiebreaker to award grants, as a way to eliminate bias in the funding process.
The agency says that the ‘physical lots’ scheme is a small step towards making the research-funding process more objective, and that it is unlikely to be used frequently. “In cases of grant applications of similar quality, bias often creeps in and influences the final call,” says Matthias Egger, president of the SNSF, which is based in Bern. “That is something we’d like to avoid.”
The SNSF distributes around one billion Swiss francs (US$1.1 billion) in research funds annually. Between 2018 and 2020, it ran a pilot scheme to test the physical-lots approach for its postdoc-mobility grants.
In March, the funder rolled out the scheme across all grant programmes, for use when deemed necessary. If referees cannot decide the ranking of two or more applications using objective criteria, they assign numbers to those applications, write the numbers on pieces of paper and place them in opaque capsules in a bowl. An SNSF official draws out the capsules one at a time, and the order decides their ranking. The agency says that in March, it used the scheme to rank 9 proposals out of 278, or 3.2%.
In 2020, Monique Beerli, a social scientist at the Free University of Brussels, won a postdoc-mobility grant of about 100,000 Swiss francs from the SNSF as a result of random allocation. Of 166 applications for the mobility grant, 82 were funded, all through physical-lots selection, according to the SNSF.
Beerli says that resorting to lots to decide between proposals of similar quality might be useful, because projects could be of equal importance but in different disciplines, which would make it difficult to choose which should be funded.
Tackling bias
The move is part of the SNSF’s drive to reduce bias in its grant-allocation process. In 2019, the agency barred its grant applicants from suggesting referees to review their proposals, after an internal study1 found that applicant-nominated reviewers were four times more likely to give favourable feedback than were those chosen by the agency.
Johan Bollen, who studies complex computer systems and networks at Indiana University Bloomington and is an advocate of a more crowd-based funding model, says that the SNSF’s lots scheme could well solve administrative obstacles for the funder, and that the initiative acknowledges shortcomings in the peer-review process. Still, he says that random selection “goes against the spirit of scientific peer review, which is directed towards recognizing merit and rewarding it”.
Adrian Barnett, a statistician and meta-science researcher at Queensland University of Technology in Brisbane, Australia, applauds the idea and says that chance already plays a part in who gets funding. “Science is inherently unpredictable,” he says. “You absolutely cannot tell what projects are going to be massive successes.”
Other funders around the world are also exploring lotteries. For instance, since 2013, the Health Research Council of New Zealand has allocated around 2% of its annual funding using lotteries to decide between projects deemed ‘fundable’, through its Explorer Grants. Last year, it increased its use of lotteries to two further funding schemes, says Lucy Pomeroy, the agency’s head of research investments and contracts.
In February 2020, Barnett, Pomeroy and their colleagues published a survey2 of people who had applied to the Explorer scheme. They found that 63% of respondents were in favour of allocating the funds randomly.
Egger says that the SNSF will keep track of researchers who obtained funding through physical lots, and those who did not, and work out how it affected their careers. “We are very keen on seeing the impact this has,” he says.
References
- 1.
Severin, A. et al. BMJ Open 10, e035058 (2020).
- 2.
Liu, M. et al. Res. Integr. Peer Rev. 5, 3 (2020).
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