Bibliographic record
Abstract
Deontic reasoning is the understanding of what may, must, or ought (not) to be done under given circumstances (Wellman & Miller, 2008). Deontic logic is often applied to social‐conventional rules (such as "set the table with the fork on the left") to give those social‐ conventions moral force, even though most people would agree that arbitrary social conventions are morally neutral. A critical question concerns whether the connection between social‐conventions and deontic logic is present in young children, or learned more slowly over time. To examine this, we provided forty‐eight (24 male; 24 female) 3‐year‐ old children with an arbitrary rule for a game involving yellow and orange balls. For half the children the rule was provided with deontic language (e.g., "you should use the orange balls"), and half were not (e.g., "use the orange balls"). Additionally, half the children were given a social‐ conventional rationale (e.g., "everyone does it that way"), while the other half were given a moral rationale (e.g., "it's the right thing to do"). If children understand that deontic logic applies even to social‐ conventional rules, then we expect that they will comply with the arbitrary game rule most when the rule is provided with deontic language and a moral rationale. This research will help parents and early childhood caregivers to better understand how young children view social‐conventional rules. This in turn will provide insight into how these social conventional rules, which are highly valued and critical to learn, might best be taught within families, day cares, and classrooms.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".