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The miracle of peer review and development in science: an agent-based model

Overview of attention for article published in Scientometrics, March 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

twitter
4 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
33 Mendeley
Title
The miracle of peer review and development in science: an agent-based model
Published in
Scientometrics, March 2017
DOI 10.1007/s11192-017-2244-y
Pubmed ID
Authors

Simone Righi, Károly Takács

Abstract

It is not easy to rationalize how peer review, as the current grassroots of science, can work based on voluntary contributions of reviewers. There is no rationale to write impartial and thorough evaluations. If reviewers are unmotivated to carefully select high quality contributions, there is no risk in submitting low-quality work by authors. As a result, scientists face a social dilemma: if everyone acts according to his or her own self-interest, the outcome is low scientific quality. We examine how the increased relevance of public good benefits (journal impact factor), the editorial policy of handling incoming reviews, and the acceptance decisions that take into account reputational information, can help the evolution of high-quality contributions from authors. High effort from the side of reviewers is problematic even if authors cooperate: reviewers are still best off by producing low-quality reviews, which does not hinder scientific development, just adds random noise and unnecessary costs to it. We show with agent-based simulations why certain self-emerged current practices, such as the increased reliance on journal metrics and the reputation bias in acceptance, work efficiently for scientific development. Our results find no proper guidelines, however, how the system of voluntary peer review with impartial and thorough evaluations could be sustainable jointly with rapid scientific development.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Luxembourg 1 3%
United States 1 3%
Netherlands 1 3%
Unknown 30 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 18%
Other 5 15%
Researcher 5 15%
Unspecified 4 12%
Student > Master 4 12%
Other 9 27%
Readers by discipline Count As %
Unspecified 7 21%
Computer Science 6 18%
Social Sciences 4 12%
Agricultural and Biological Sciences 3 9%
Engineering 3 9%
Other 10 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 April 2017.
All research outputs
#3,843,030
of 9,278,926 outputs
Outputs from Scientometrics
#665
of 1,402 outputs
Outputs of similar age
#101,054
of 254,374 outputs
Outputs of similar age from Scientometrics
#32
of 69 outputs
Altmetric has tracked 9,278,926 research outputs across all sources so far. This one has received more attention than most of these and is in the 58th percentile.
So far Altmetric has tracked 1,402 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has gotten more attention than average, scoring higher than 51% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 254,374 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.