Configurational Research in Teams
Bibliographic record
Abstract
Teams are complex systems in which multiple individual members, themselves containing multiple characteristics, are embedded (Arrow, McGrath, & Berdahl, 2000). Team research has historically been dominated by a focus on what is shared across these multiple components, such as aggregations of team member characteristics or perceptions of team processes operationalized through calculating agreement statistics and means. Instead, configurational perspectives on teams and their members recognize the complexity of teams by focusing on the uneven and disproportional effects of member characteristics or processes on team outcomes (Kozlowski & Klein, 2000). Understanding such effects is essential for building upon traditional approaches using shared team constructs to create new knowledge about how teams create synergy amid seemingly limitless potential for chaos and conflict. Moreover, adopting configurational perspectives on teams is increasingly tractable with advances in configurational methodologies (e.g., social networks, faultlines, latent profile analysis, qualitative comparative analysis, and attribute alignment). As such, this panel symposium will 1) generate awareness of configurational perspectives and methods used in team research, 2) share recent advances in the field, and 3) create an opportunity for researchers who are actively working in or interested in this area to collaborate. The format will include an introduction to the topic, brief presentations from the panelists, and an informal Q&A session involving the panelists, their collaborators, and the audience.
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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".