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dc.contributor.authorRobinson-Garcia, Nicolás
dc.contributor.authorCostas, Rodrigo
dc.contributor.authorSugimoto, Cassidy R.
dc.contributor.authorLarivière, Vincent
dc.contributor.authorNane, Gabriela F.
dc.date.accessioned2021-11-03T13:46:22Z
dc.date.availableNO_RESTRICTIONfr
dc.date.available2021-11-03T13:46:22Z
dc.date.issued2020-10-28
dc.identifier.urihttp://hdl.handle.net/1866/25792
dc.publishereLife Sciences Publicationsfr
dc.rightsCe document est mis à disposition selon les termes de la Licence Creative Commons Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleTask specialization across research careersfr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. Faculté des arts et des sciences. École de bibliothéconomie et des sciences de l'informationfr
dc.identifier.doi10.7554/eLife.60586
dcterms.abstractResearch 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.fr
dcterms.languageengfr
dcterms.relationhttp://doi.org/10.5281/zenodo.3891055fr
UdeM.ReferenceFournieParDeposanthttps://doi.org/10.7554/eLife.60586fr
UdeM.VersionRioxxVersion publiée / Version of Recordfr
oaire.citationTitleeLifefr
oaire.citationVolume9fr


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Ce document est mis à disposition selon les termes de la Licence Creative Commons Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0 International License.
Usage rights : Ce document est mis à disposition selon les termes de la Licence Creative Commons Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0 International License.