Postdoctoral scientists are mentors, and it is time to recognize their work
Notice bibliographique
Résumé
Academia often fails to recognize the important work that supports its functioning, such as mentoring and teaching performed by postdoctoral researchers.This is a particular problem for early-career researchers, but opportunities exist to improve the status quo.One adjective commonly applied to postdoctoral scientists (postdocs) and their work is "invisible": invisible scholars [1], invisible innovators, invisible mentors.These metaphors describe the reality of the labor performed by many postdocs; these tasks are often expected of them by the demands of the academic job market but seldom formally credited to them.Owing to their (often) dual role as employees and trainees, they are expected to fulfill the obligations of both while reaping the benefits of neither.For example, postdocs are not always eligible to apply for independent research funding and, therefore, need to split credit with researchers with more job security.Moreover, when postdocs contribute to teaching, they are not always listed as instructors of record (designated as in charge of the course), and, therefore, this work may not be consistently recognized in job applications.They are also no longer eligible for training, grants, or fellowships exclusive for students, and in many institutions, postdocs do not have health benefits coverage, unlike graduate students and faculty, for whom coverage is often mandatory.For example, in the province of Que ´bec, Canada, health insurance coverage for postdocs can vary according to immigration status, history of foreign residency, medical history, immigration status of spouse or common-law partner, intercountry agreements, specific job title, and university partnerships with private insurance companies.For postdocs wanting to pursue an academic career, this lack of recognition can put them at a serious disadvantage, as uncredited work can come at the expense of contributing to research projects.This conundrum is particularly true when it comes to mentoring graduate students.Postdocs are highly trained, up-to-date on the literature, have a fresh eye on the state of the art in their field, and are often leading experts in emerging methodologies and approaches.In fact, data suggestAU : PleasenotethatasperPLOSstyle; }data}takespluralverb:Hence; }Infact; datasuggeststha that over a 5-year period, postdocs in the life sciences outpublish graduate students and faculty [2], which demonstrates their familiarity with cutting-edge scientific research subjects and practices.Being early in their career, they have likely very recently experienced
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,078 | 0,198 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,012 | 0,016 |
| Communication savante | 0,018 | 0,015 |
| Science ouverte | 0,003 | 0,017 |
| Intégrité de la recherche | 0,006 | 0,023 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,027 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».