Evidence map›Paper›PMID 33112232›Full record

ArticleeLife2020

Task specialization across research careers.

Nicolas Robinson-Garcia, Rodrigo Costas, Cassidy R Sugimoto, Vincent Larivière, Gabriela F Nane

Abstract read
In one paragraph

Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nicolas Robinson-GarciaDelft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-0585-7359
Rodrigo CostasCentre for Science and Technology Studies, Leiden University, Leiden, Netherlands.ORCID 0000-0002-7465-6462
Cassidy R SugimotoSchool of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, United States.
Vincent LarivièreÉcole de bibliothéconomie et des sciences de l'information, Université de Montréal, Montreal, Canada.
Gabriela F NaneDelft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands.

Funding

European Commission 707404
6 · The paper itself

Abstract

Research careers are typically envisioned as a single path in which a scientist starts as a member of a team working under the guidance of one or more experienced scientists and, if they are successful, ends with the individual leading their own research group and training future generations of scientists. Here we study the author contribution statements of published research papers in order to explore possible biases and disparities in career trajectories in science. We used Bayesian networks to train a prediction model based on a dataset of 70,694 publications from PLoS journals, which included 347,136 distinct authors and their associated contribution statements. This model was used to predict the contributions of 222,925 authors in 6,236,239 publications, and to apply a robust archetypal analysis to profile scientists across four career stages: junior, early-career, mid-career and late-career. All three of the archetypes we found - leader, specialized, and supporting - were encountered for early-career and mid-career researchers. Junior researchers displayed only two archetypes (specialized, and supporting), as did late-career researchers (leader and supporting). Scientists assigned to the leader and specialized archetypes tended to have longer careers than those assigned to the supporting archetype. We also observed consistent gender bias at all stages: the majority of male scientists belonged to the leader archetype, while the larger proportion of women belonged to the specialized archetype, especially for early-career and mid-career researchers.

Indexed as

AuthorshipBayes TheoremBibliometricsFemaleHumansJournal Impact FactorMaleModels, StatisticalPeriodicals as TopicResearchResearch PersonnelSex FactorsSpecializationTime Factorsbiochemistrycareers in sciencechemical biologygender biasmeta-researchnonescience of science

Identifiers

PMID33112232
PMCPMC7647403

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.