GROUP FORMATION IN A SIMULATED SCAVENGER HUNT: HOW BIG AND DIVERSE SHOULD A SUCCESSFUL GROUP BE?
Bibliographic record
Abstract
Male and female university students (121) received 1 of 8 scenarios describing a hypothetical scavenger hunt. Subjects could form a group to assist them in the hunt or work alone. Groups could be homogeneous or diverse in terms of gender, age, and familiarity. Completion of the task would result in a prize of 1,000. Subjects indicated how this payoff would be distributed to the group. Twenty minutes or 90 minutes were given to find the designated objects. Their task was also varied in terms of the number of items that needed to be discovered (4 items or 8 items) and whether or not these items were locally available (on the campus of the university they attended) or were geographically dispersed throughout the surrounding city. Subjects engaged in a two-stage decision process. In the first stage, a decision was made concerning the size of the group formed (if any) and its homogeneity or diversity in membership. Larger groups were chosen when 8 versus 4 items were gathered and when the items were geographically dispersed. Groups tended to be more diverse when items were dispersed rather than locally concentrated and when time was short (20 minutes). Payoff division was a second-stage decision. Equity versus equality in distribution was more likely to occur when groups were large and diverse in membership.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".