From “Taking Orders” to Being a “Self-starter”: Research Assistants and Postdoctoral Fellows’ Skill Development in Large Collaborative Research Projects
Notice bibliographique
Résumé
Fellows' Skill Development in Large Collaborative Research Projects 2 Like many nations, Canada competes on the global market as a knowledge-based economy.As such, the country needs employees with skills in self-direction, communication, adaptability, critical thinking, project management, and collaboration as well as technical skills and content knowledge to innovate and compete with other post-secondary institutions, companies, organizations, and countries (Bilodeau; Mitacs; Niemczyk, Expanding; SSHRC, "Report").However, while there has been increasing use of collaborative projects in graduate course work, graduate and postdoctoral training remains primarily solitary in nature, which means limited opportunities for these individuals to fully develop these skills (Barry et al.; Bohen and Stiles).But what skills can a graduate student and postdoctoral fellow develop through their course work and associated training in school and beyond?What are the best ways to gain these?One possible avenue of experience is as graduate research assistants (RAs) and postdoctoral fellows (postdocs) on faculty research projects (Niemczyk, "Preparing").Students and postdocs can undertake a variety of research tasks, such as literature reviews, data collection and analysis, research write ups, experiments, and others.These faculty research projects are also becoming more collaborative in nature as research questions become more complex and require an approach that brings together teams of people with different skill sets and knowledge (He and Jeng; Kosmützky).This means that RAs and postdocs can gain experience in collaboration and project management as well as important content knowledge and methodologies (SSHRC, "Report").These opportunities prepare students and postdocs for careers in the academy as well as private, public, and nonprofit sectors.This context raises questions about the type of experiences that RAs and postdocs gain within collaborative research projects funded through faculty researchers' grants.There is little research on the research assistance and postdoctoral training as "educational spaces where theory meets practice" (Niemczyk, Expanding 1).What training do they receive?How do they develop skills needed for the particular research project and employment beyond it?This paper will contribute to this discussion by examining the lived experience of research assistants and postdoctoral fellows in the Implementing New Knowledge Environments (INKE) project, a large, long-term research grant on electronic books.Context Training research students and postdoctoral fellows is necessary for success in the knowledge-based economy within the university, public, private and nonprofit sectors and plays an important role generating new knowledge and innovation (CAGS; Niemczyk, Case Study, Expanding, and "Preparing").They will use their professional skills, including academic skills related to their discipline, research ability and teaching, and broader transferrable skills such as communication, management, adaptability, critical thinking, collaboration, knowledge transfer, and ethics in academic and non-academic settings (CAGS; Lapointe; Mitacs; Niemczyk, Case Study; Nowell et al.; Pollon et al.; Rose; SSHRC, "Guidelines").Given this need, it is imperative to train RAs and postdocs in research and other skills through hands-on research opportunities (Niemczyk, Expanding).
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 | 0,043 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,008 |
| Communication savante | 0,011 | 0,008 |
| Science ouverte | 0,002 | 0,013 |
| Intégrité de la recherche | 0,003 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
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 ».