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Task specialization across research careers.

Nicolas Robinson-Garcia1, Rodrigo Costas2,3, Cassidy R Sugimoto4

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Summary
This summary is machine-generated.

This study analyzed author contributions to reveal career path disparities in science. Findings show gender bias, with men more often in leader roles and women in specialized roles, impacting career longevity.

Keywords:
biochemistrycareers in sciencechemical biologygender biasmeta-researchnonescience of science

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Area of Science:

  • Bibliometrics
  • Scientometrics
  • Sociology of Science

Background:

  • Traditional research careers follow a linear progression from trainee to independent investigator.
  • Author contribution statements offer insights into scientific collaboration and career trajectories.
  • Disparities in scientific career progression and gender bias are persistent concerns.

Purpose of the Study:

  • To investigate potential biases and disparities in scientific career paths using author contribution data.
  • To profile scientists across different career stages (junior, early-career, mid-career, late-career) based on their contributions.
  • To identify gender-based patterns in career archetypes and their association with career length.

Main Methods:

  • Utilized Bayesian networks to train a prediction model on a large dataset of PLoS publications and author contributions.
  • Analyzed contribution statements from 70,694 publications involving 347,136 authors.
  • Applied archetypal analysis to categorize 222,925 authors across 6,236,239 publications into distinct career profiles.

Main Results:

  • Identified three main scientist archetypes: leader, specialized, and supporting.
  • Observed that leader and specialized archetypes are associated with longer careers compared to the supporting archetype.
  • Detected significant gender bias: men predominantly in leader archetypes, women in specialized archetypes, particularly in early and mid-career stages.

Conclusions:

  • Scientific career paths are not monolithic and can be categorized into distinct archetypes.
  • Gender bias is evident across all career stages, influencing archetype assignment and potentially career duration.
  • Understanding these archetypes and biases is crucial for promoting equity and diversity in science.