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Model-based evaluation of scientific impact indicators.

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Summary
This summary is machine-generated.

Simple citation counts effectively measure researcher ability, while h-index and g-index show potential when productivity is considered. Logarithmic citation units offer comparable performance to these metrics.

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Area of Science:

  • Bibliometrics
  • Scientometrics
  • Research Assessment

Background:

  • Evaluating scientific impact relies on bibliometric indicators.
  • Existing indicators may contain biases and not fully capture researcher contributions.
  • A controlled environment is needed to rigorously compare these metrics.

Purpose of the Study:

  • To compare the performance of various bibliometric indicators for assessing individual researcher impact.
  • To identify which indicators best reflect intrinsic scientific ability and productivity.
  • To evaluate the fairness of normalized indicators across different career stages.

Main Methods:

  • Utilizing artificially generated bibliometric data from a calibrated citation dynamics model.
  • Comparing standard indicators like average citation count, h-index, and g-index.
  • Assessing indicator performance in a controlled simulation to avoid real-world database biases.

Main Results:

  • Average citation count effectively captures intrinsic researcher ability across career lengths.
  • h-index and g-index show potential when productivity is factored in, but normalized versions can be unfair.
  • Indicators using logarithmic citation units perform comparably to h- and g-indices.

Conclusions:

  • Simple citation metrics can be reliable for assessing research ability.
  • Careful consideration of indicator normalization is crucial for fair comparisons.
  • The developed framework offers a robust tool for evaluating impact metrics and can be extended.