The Effect of a Context‐Specific Primed Goal on Goal Commitment and Team Performance
Bibliographic record
Abstract
The effect of a context‐specific prime for cooperation on goal commitment and team performance were examined. In the first experiment, the participants (n = 139) performed the Lost on the Moon simulation (Hall & Watson, 1970) individually and as a team (n = 50). The teams were randomly assigned to a condition where they were assigned the same goal. They were then primed (n = 23) through a photograph of cooperation or to the control condition (n = 27). Consistent with NASA’s directions for performing the simulation, performance was measured by how well a team performed relative to the answers of experts, namely, staff at NASA. The results showed that a primed behavioural goal to cooperate has a positive effect on team performance. These results were replicated in a second and third experiment involving a social dilemma where both a pro‐social, group‐centric goal and a pro‐self, egocentric goal could be self‐set for the amount of points to make. Thus the positive effect of a goal primed for cooperation on a team’s performance was shown to be robust even when there was an explicit mixture of cooperative and competitive incentives. This finding was replicated in a third experiment with actual money. Consistent with goal setting theory, commitment to the team’s goal moderated the primed goal‐performance relationship.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".