TEAM PERFORMANCE AND EFFICIENCY, TOWARD A CONCEPTUAL FRAMEWORK
Bibliographic record
Abstract
Research is knowledge-intensive work, which implies performing complex and ambiguous activities in order to develop or create knowledge. The context of researchers is marked by a few trends: 1) an increasing complexity of research objects, which requires more and more to adopt multidisciplinary approaches; 2) emerging institutional pressures under which the academic career is more and more dependent on scientific researchers' results. Consequently, individuals tend to join teams in order to maximize the cumulative advantage of collaboration on research performance and effectiveness. The last decades have thus shown a significant shift in the way of conducting research, from individual based work to an organization of work in teams of scientists. However, despite the importance of working groups for research, no synthesis of empirical evidence has yet been done on academic researchers. This scoping review allows to advance knowledge on the synthesis of research team performance and effectiveness and to construct an evidence-based conceptual framework. It allows to identify 28 articles, whose analysis lead to the identification of more than 15 groups of operational definitions of performance and effectiveness, and more than 25 groups of determinants linked to three levels of aggregation (project, team, and institutional/organizational levels).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".