Keeping up with the Times: Equity Issue is Now Added to Our Self‐Reflection Worksheet for Improving Scientific Mentoring
Notice bibliographique
Résumé
Mentoring is a core activity for many scientists, and yet few of us have had any formal training in how to do it well. Most of us plod along, subconsciously drawing on our own experiences of having been mentored in the past, and relying on “learning by our mistakes.” Formal reflections on the goals of mentoring, and how they can best be achieved, are rare in the literature, and yet mentoring is a fundamental process not just in the scientific training of young researchers, but also in their personal development and in building the social fabric of the scientific community. Some years ago, my colleagues Val Eviner, Sarah Hobbie, and I surveyed the mentees of Professor Terry Chapin and developed a synthesis entitled “The qualities and impacts of a great mentor — and how to improve your own mentoring.” It was originally published in the ESA Bulletin 94(2), April 2013, pages 170–176. On the basis of what we learned from that survey and our further reflections, as well as a review of the sparse literature on this topic, the above article concluded with a two-page self-assessment worksheet aimed at comprehensively identifying the fundamental features of good mentoring and providing a useful reflection guide for anyone interested in analyzing and improving their mentoring practices. The worksheet is entitled: “Mentoring self-assessment reflection exercise: Are you aware of these fundamental features of good mentoring? Which features should you focus on most to be a better mentor?” Since formulating that worksheet, sensitivities to the issues of equity, diversity, inclusion, justice, and Indigeneity have been greatly heightened among the public in many countries. Guides to improve mentoring should embrace such positive social changes. Accordingly, the revised self-assessment worksheet available here has been updated to include a new reflection question specifically focused on equity, diversity, inclusion, justice, and Indigeneity so as to raise awareness among mentors of the relevance of these important issues (Table 1; easily printable PDF version available as Appendix S1). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».