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Where should I send it? Optimizing the submission decision process.

Santiago Salinas1, Stephan B Munch2

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Summary
This summary is machine-generated.

Scientists can optimize manuscript submissions using Markov decision processes. Modeling choices based on prestige, acceptance rates, and time can guide authors to the best journal for maximizing impact and minimizing revisions.

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Area of Science:

  • Ecology and Evolutionary Biology
  • Scientific Publishing
  • Decision Science

Background:

  • Manuscript submission decisions involve complex trade-offs between journal prestige, acceptance probability, turnaround time, audience, and impact factor.
  • Existing methods for journal selection are often heuristic and do not account for the sequential nature of the submission process.

Purpose of the Study:

  • To develop a quantitative framework for optimizing manuscript submission strategies using Markov decision processes.
  • To create models that balance maximizing citations with minimizing resubmissions or review time.

Main Methods:

  • Developed two Markov decision process models: one maximizing citations, another balancing citations with time or resubmissions.
  • Parameterized models using data on acceptance probability, submission-to-decision times, and impact factors for 61 ecology journals.

Main Results:

  • Optimal submission sequences depend on author priorities regarding time to acceptance and number of resubmissions.
  • Journals like Ecology Letters, Ecological Monographs, and PLOS ONE emerged as potentially optimal starting points in certain scenarios.

Conclusions:

  • A Markov decision process framework provides a data-driven approach to manuscript submission strategy.
  • This quantitative analysis offers guidance for authors seeking to optimize their publication process based on individual objectives.