Magnitude of resource and reputational concern impact generosity and deception in children
Bibliographic record
Abstract
Introduction In a bargaining process, there are factors impacting the outcome (i.e. gain versus loss). Of the important factors is the informational asymmetries between bargainers which could provide a more powerful position for whom has information that the other partner has not as well as magnitude of resource. Objectives Thus, in a modified two-round, 4 trials (different magnitudes) bargaining paradigm, we investigated deception in children when there is no chance of deception revelation (first round) and when there is a chance of deception revelation (second round). Methods One hundred and forty one healthy schoolchildren (90 boys and 51 girls) between age of 7 and 12 participated in the current study. We designed a modified version of the bargaining paradigm based on the experimental design by members of the junior faculty workshop in the conflict management division at the 1995 academy of management meetings in Vancouver, BC. Variables included earning amount, deception frequency, real generosity and pretend generosity. Results Using paired-samples T-test we showed that there were significant differences between two rounds in earning amount, deception frequency and real generosity. We administered separate one-way ANOVA with repeated measure on 4 different conditions (bank amounts. We found that, the main effect of condition was significant for real generosity (in both round), for pretend generosity (in first round) and for deception amount (in both round). Conclusions We found that revelation (reputation concerns) decreases deception and increases generosity. Moreover, lesser magnitudes increase generosity and decrease deception and vice versa. Disclosure of interest The author has not supplied his/her declaration of competing interest.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".