The Effects of Team Diversity in Knowledge Sourcing Scope and Individual Learning Mode: A Multi-Level Approach.
Bibliographic record
Abstract
A team member acquires knowledge to successfully complete team tasks with other members by accessing his/her available knowledge sources. In this knowledge process, individual members can use (1) internal knowledge source from knowledgeable coworkers or formal sources within their organizational boundaries. They can also rely on (2) external knowledge source by networking with external experts or informal sources outside their organizations. To learn knowledge acquired from internal/external sources and apply it to team tasks, individual members adopt two different learning modes: (1) exploitation by repeatedly adopting and applying the existing knowledge and (2) exploration by idiosyncratically developing their own understanding. Regarding such two dimensions of knowledge processing (i.e., knowledge sourcing and individual learning), the social categorization and the information/decision-making perspectives suggest that team diversity has different effects on individual performance, which consists of task-relevant performance and creative performance. Moreover, prior studies using single-level research designs have overlooked the multi-level nature of knowledge processing in which individual members are influenced by one another. To compromise the different suggestions from these competing theories and to explain the contextual effects of team diversity in knowledge processing, this study conceptualizes a two-dimensional team diversity in terms of knowledge sourcing scope and individual learning mode. We then hypothesize its top-down effects on individual knowledge processing in work groups. The multi-level approach suggested in this study might advance our understanding of team diversity in knowledge processing and its effects on individual performance by integrating the cross-level associations into a single study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".