How Prospective Direct and Indirect Reciprocity Influence 4- to 6-Year-Old’s Sharing
Bibliographic record
Abstract
Although an abundance of evidence support that preschoolers use reciprocity as a reply to others, lesser is known about how they use this strategy in initiating social interactions. Aiming to explore this question, the current study focused on two forms of prospective reciprocity, direct and indirect (downstream) reciprocity. Two studies were conducted in which the chance for prospective reciprocity was implicit (study 1) and explicit (study 2). Specifically, 4- to 6-year-olds were asked to share stickers with a non-shown recipient, a shown recipient, or a non-shown recipient while a witness was observing. In study 1, preschoolers did not know whether the shown recipient/witness would interact with them later. In study 2, they knew the shown recipient/witness would be asked to share with them subsequently. Results revealed that, despite the implicit/explicit chance of prospective reciprocity, preschoolers shared more in the prospective direct reciprocity condition than the control/prospective indirect downstream reciprocity conditions. In addition, comparing the two studies found no difference found between the implicit and explicit situations. Overall, these findings indicate that preschoolers have taken direct reciprocity, rather than indirect (downstream) reciprocity in guiding their initial sharing with others. Implications of these findings are further 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 | 0.002 | 0.005 |
| 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) | 0.002 | 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".