Team Dynamics and Learning Opportunities in Social Science Research Teams
Bibliographic record
Abstract
Although the contemporary research environment encourages knowledge generation through research collaboration rather than individualized projects, limited scholarly attention has been devoted to the practice of collaboration within research teams. This paper presents a qualitative analysis of team dynamics and learning opportunities within four social science research teams. The findings reveal similarities and differences in leadership style and interaction approaches that affected how research was undertaken and the possibilities for team members to learn from each other. The snapshots provide models for other research teams that extend situated learning theories and the existing research base about collaboration, research teams, and research leadership. Key words: research teams, research leadership, researcher development, situated learning Bien que le milieu actuel de la recherche encourage la génération des connaissances par la collaboration en recherche plutôt que par les projets individuels, les universitaires ont accordé peu d’attention à la pratique collaborative au sein des équipes de recherche. Cet article présente une analyse qualitative de la dynamique des équipes et des occasions d’apprentissage au sein de quatre équipes de recherche en sciences sociales. Les résultats révèlent des ressemblances et des différences dans le style de leadership et les démarches d’interaction qui ont eu une influence sur la façon dont la recherche a été entreprise et sur les possibilités pour les membres des équipes d’apprendre l’un de l’autre. Les aperçus offrent des modèles pour d’autres équipes de recherche et contribuent aux théories de l’apprentissage contextualisé et à la base de recherche portant sur la collaboration, les équipes de recherche et le leadership en recherche. Mots clés : équipes de recherche, leadership en recherche, développement des chercheurs, apprentissage contextualisé
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".