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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Modelling science trustworthiness under publish or perish pressure.

David Robert Grimes1,2, Chris T Bauch3, John P A Ioannidis4,5,6,7,8

  • 1School of Mathematics and Physics, Queen's University Belfast, BT7 1NN, UK.

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Publication incentives can harm scientific trustworthiness. Our model shows that funding allocation based on publications, false positive rates, and pressure for positive results decrease science integrity, risking public trust.

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Area of Science:

  • Scientific publishing and research integrity.
  • Bibliometrics and scientometrics.
  • Philosophy of science.

Background:

  • The current scientific reward system, emphasizing publication quantity, may incentivize careless or fraudulent research.
  • Top-tier journals' preference for novel, positive findings exacerbates the reproducibility crisis.
  • Lack of quantitative models on factors influencing scientific trustworthiness hinders problem assessment.

Purpose of the Study:

  • To develop a phenomenological model to analyze factors affecting the trustworthiness of scientific publications.
  • To quantify the impact of publication incentives and journal biases on scientific integrity.
  • To explore strategies for mitigating the propagation of irreproducible research.

Main Methods:

  • A simple phenomenological model simulating cohorts of scientists (diligent, careless, unethical).
  • Funding allocation based on published scientific outputs.
  • Analysis of the influence of false positive rates and pressure for positive results on trustworthiness.

Main Results:

  • Scientific trustworthiness is significantly influenced by the false positive rate and the pressure to publish positive results.
  • Decreasing available funding negatively impacts the resulting trustworthiness of published science.
  • The model provides insights into the propagation of irreproducible research.

Conclusions:

  • Reforming publication incentives and funding models is crucial for enhancing scientific integrity.
  • Addressing the bias towards positive results in high-impact journals is essential for combating the reproducibility crisis.
  • Quantitative modeling can inform strategies to improve the trustworthiness and reliability of scientific findings.