Abstract Reviewer Guidelines

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BigSurv20 Conoravirus Monitoring Decision: BigSurv20 will NOT be held in person in Utrecht in November 2020. For more information, visit the BigSurv20 home page

Please note that the text below reflects conference planning information prior to the Coronavirus monitoring decision. As a result, it may not include the most accurate and up-to-date information. The BigSurv20 Scientific Committee plans to update this webpage in June 2020.

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The BigSurv20 planning Scientific Committee would like to thank you for agreeing to review abstract submissions for the BigSurv20 conference. We have provided further instructions and recommended considerations for the review process below. If you have any questions, please do not hesitate to contact us at [email protected].

Instructions for Reviewers: Please complete the following steps for the abstract review process. Each reviewer will be assigned 4, 5, or 6 abstracts to review. As a reminder, all abstract reviews must be finished by 11:59 p.m. ET on Thursday, March 19. All abstracts have to be treated confidentially and must not be used for purposes other than the abstract review process.

  1. Log in to the BigSurv20 conference management system to access and review abstracts. Use the same login information (email and password) as when you submitted your abstract.
  2. Locate your assigned abstracts under the "Submit/edit reviews" tab at the bottom of the page.
  3. Read the abstract and provide a rating and an overall recommendation.

    On a scale from 1 to 5, with 1 indicating "unacceptable" and 5 indicating "outstanding" please rate each abstract according to the following criteria.
  • Readability: Is the abstract well written and clearly organized? (1-5)
  • Relevance: Does this abstract describe research that either combines survey research and big data, discusses emerging analysis techniques, has the potential to extend to larger or complex data sets, or aligns with one of the presentation track categories? (see also https://www.bigsurv20.org/abstracts) (1-5)
  • Technical quality: Are the research methods sound, and do the results appropriately address the research questions? (1-5)
  • Novelty: Is the research a novel contribution to the community/do we learn something new? (1-5)
  • Implications: Will this research stimulate discussion at the conference? Does it draw broader implications that would advance the literature? (1-5)
  • Overall: How would you rate the abstract submission overall? (1-5)

Rating notes:
Remember that not all papers may fit directly with your area(s) of expertise. If you find that this is the case, your feedback is still valuable. These kinds of reviews may emphasize the clarity with which the author communicates to audiences outside of their sub-discipline, the quality of writing, and/or the general appeal of the contribution.

After providing the ratings, please select one of the following recommendations:

  • Accept presentation and recommend for publication in the preferred publication outlet
  • Accept presentation and instead recommend for publication in the other publication outlet
  • Accept presentation, but do not recommend for publication outlet
  • Accept as poster
  • Reject

Recommendation notes:
(1) These recommendations are slightly adapted for the student paper competition.
(2) When making your recommendation, please consider that the special issues/conference proceeding will highlight the best empirical studies worthy of inclusion in a peer-reviewed journal/proceeding and is intended for a wider audience of researchers interested in computational social science.

  1. Once you have selected a recommendation, please confirm by clicking on the “Submit Review” button.
  2. Review the remaining abstracts following steps 3, 4, and 5.

 

See you in Utrecht in November!
Antje Kirchner and Peter Lugtig
On behalf of the BigSurv20 Scientific Committee (Amelia Burke-Garcia, Trent Buskirk, Ana Lucía Córdova Cazar, Piet J.H. Daas, John Finamore, Craig Hill, Don Jang, Lilli Japec, Stas Kolenikov, Daniel Oberski, Barry Schouten, Nan Zhang)