Team Cohesion and Individual Productivity
Bibliographic record
Abstract
This study investigated the potential moderating effects of productivity norms and identifiability of effort on the cohesion-performance relationship in team sports. The design was a 2 (high cohesion, low cohesion)× 2 (high productivity norm, low productivity norm)× 2 (high identifiability of an individual’s effort, low identifiability of an individual’s effort) factorial. Each participant (n = 324) read one of eight scenarios, with cohesion, norms, and identifiability systematically rotated, and indicated the probability that the individual would train during the off-season. An ANOVA showed a main effect for cohesion, F( 1, 316) = 113.44, p < .0001, and norms, F( 1, 316) = 19.61, p < .0001), and an interaction between cohesion and norms, F( 1, 316) = 7.35, p = .007. The probability of off-season training was significantly higher for the high-cohesion-high-norms scenario than for the high-cohesion-low-norms scenario, with no differences under conditions of low cohesion. Directions for future research are discussed.
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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 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".