Victorious and Hierarchical, Defeated and Flat: When Team Hierarchies Change Following Success
Bibliographic record
Abstract
Hierarchies emerge as collectives attempt to organize toward successful performance. Consequently, the relationship between team hierarchies and team performance has been widely explored. However, extant research has conceptualized hierarchies primarily as static entities, examining how a hierarchy at one point in time relates to team performance at a later point in time. This approach tends to neglect a fundamental reality about hierarchies: they change. In this paper, we develop a theory about the dynamic relationship between team hierarchies and performance by suggesting that performance also serves as an important input that actively shapes hierarchies. Drawing on the exploration and exploitation framework, we argue that successful (unsuccessful) performance triggers exploitation (or exploration) which leads to an increase (decrease) in the degree of a team’s hierarchy, an effect that is especially strong when the team already has a high degree of hierarchy. Utilizing recent advances in behavioral sensors as a proxy for team processes, we exploit fine-grained passing data from the National Basketball Association (NBA) to test our hypotheses. We observe how a team’s hierarchy changes on a game-by game basis based on prior performance. Winning is associated with an increase in hierarchy, and this relationship is stronger among teams already high in hierarchy. We then explore the full cycle of performance–hierarchy–performance by testing how hierarchy affects subsequent performance. We conclude by highlighting how our theory and findings extend prevailing discussions in the hierarchy literature.
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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".