Notice bibliographique
Résumé
Little did we know that we would be forced to do things differently because of a global pandemic when the idea for this special issue/section was developed in 2019! The Black Lives Matter movement in 2020 reinforced that action and not just talk is needed—we cannot continue to do things just because “we have always done it that way”. Effective change requires frequent, honest review and feedback, with revisions where needed to meet the desired outcome. All too often changes are made with the best intentions but result in unintended consequences (e.g., the cobra effect). The articles in this special issue section cover a variety of topics. The first two papers[1, 2] discuss the need for, and ways to achieve, inclusivity in the workplace. Diverse organizations are more successful, and male allyship can significantly advance culture change to reach equality for all underrepresented groups. The next four papers discuss moving past conventional teaching approaches and provide many ideas, including how polarity management maximizes team potential and leads to innovation[3]; real-world examples and team-based test retakes promote student engagement and success[4]; and open-ended laboratory problems[5] and multi-disciplinary capstone projects,[6] both with an industrial focus, promote learning and desirable engineering traits but require different course logistics. The final three papers discuss how to properly collect, analyze, and interpret data, not fear “failed” experiments, and avoid zombie ideas[7]; the benefits and challenges of disseminating results through open science[8]; and how to stimulate innovation in graduate students through open-ended exploration.[9] These topics are complementary, and, though the manuscripts were written independently, you will see many common threads throughout the articles. Thank you to all the authors who are clearly passionate about their work, as well as to the reviewers who provided thoughtful feedback. I would like to especially thank Prof. Suzanne Kresta for discussions and contributor recommendations in the development phase of this project. In the spirit of this special issue section, some articles reflect the personal perspectives of their authors and rely less on scientific evidence than traditional papers, but I hope that all the articles will encourage discussion, perhaps create some discomfort, and stimulate everyone to do things differently.
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 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 | 0,000 | 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».