Networks and Diversity in the New Era of Organizational Teams
Bibliographic record
Abstract
Taking a network perspective to study teams has been popular and fruitful in the past decades. Yet, the changing nature of how work teams are organized and managed in the new era brings unprecedented challenges to this line of work. For example, nowadays, many teams have fuzzy boundaries. And social exchanges and collaborations between groups are far more frequent and intensive than they traditionally were. Teams are also becoming increasingly diverse due to the globalization trend and the recognition of the value of diversity. These changing features of teams are likely to influence or interact with intra- and inter-team networks and exert a collective, integrated impact on individual members’ and teams’ cognitions, behaviors, and outcomes, which have not been thoroughly understood and examined. Our symposium highlights the recent efforts to investigate new emergent features of teams and explore how they interact with networks within and between teams. Two papers directly tap into the members’ social relations within and between teams, the diversity of these social relations, and associated team performance outcomes. Another two papers look at dynamic entrepreneurial teams, where each team constitutes the entire organization. Each explores a different element of diversity as a function of how networks are strategically used in these budding firms. The fifth paper switches the gear to focus on individuals’ intrapersonal diversity and network structural features and provides insights into how their linkage may shape team dynamics. Complementarities of Members’ Structural Roles in Team Success: The Moderating Role of Experience Author: Shihan Li; Heinz College - Carnegie Mellon U. Author: Brandy Aven; Carnegie Mellon U. The Social Underpinnings of Effective Organizational Interteam Relations Author: Martin J. Kilduff; UCL School of Management Author: Andreas Wilhelm Richter; U. of Cambridge Author: Ronald Clarke; Rennes School of Business Multicultural Experience and Social Network Brokerage Author: Eva Hsin-Lian Lin; London Business School Author: Raina A. Brands; UCL School of Management Author: Adrienne Wood; U. of Virginia Author: Adam M. Kleinbaum; Dartmouth College, Tuck School of Business Showcasing strategies: The Role of Entrepreneurial Networking in Quest for Venture Capital Funding Author: Damiano Maria Morando; Imperial College Business School Author: Anne L.J. Ter Wal; Imperial College Business School Author: Stefano Breschi; Bocconi U. Recruiting for your team: Network hiring and match-specific performance in firms Author: Ines Black; - Author: Sharique Hasan; Fuqua School of Business, Duke U.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.006 |
| 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".