Outcome interdependence in sport teams: from theoretical foundations to future research directions
Bibliographic record
Abstract
An ever-present variable across levels of sport participation is outcome interdependence. The ways in which team members depend on each other, however, are not well-understood in sport teams. We revisit the conceptualization of outcome interdependence by arguing that the current theoretical foundations (Social Interdependence Theory and Interdependence Theory) lack the context needed to better understand sport team dynamics. To help alleviate the issue, we propose a conceptual model for future sport team research. Our description of the conceptual model contributes to a sport team-specific understanding of interdependence by emphasizing that: (a) the explicit structure of sports and sport teams form outcome interdependence; (b) unique multi-level motives control the dense web of outcome interdependence; (c) outcome interdependence is often dictated by third-party decisions; and (d) the recursive nature of the sport team environment promotes the evolution of outcome interdependence. We identify goal-related outcome interdependence as a future research direction by specifying (a) the integration of our conceptual model with goal interdependence theorizing, (b) the development of measurement tools, and (c) an appropriate method for studying goal interdependence. By refining the approach to outcome interdependence in sport teams, researchers will better understand a foundational element of sport team dynamics.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".