"You Have to Listen to Me Because I'm in Charge": Explicit Instruction Improves the Supervision Practices of Older Siblings
Bibliographic record
Abstract
OBJECTIVES: Sibling supervision increases young children's risk of unintentional injury. Both noncompliance by the supervisee and insufficient supervision contribute to this risk. The current study examined whether explicitly instructing older siblings to supervise their younger siblings and prevent specific risky behaviors improves their supervision practices. METHODS: Supervisees and older siblings were placed together in a playroom. One group of older siblings were given explicit instructions not to allow the supervisee to engage in specific risk behaviors, whereas a second group was not. RESULTS: Informing older siblings that they were "in charge" resulted in a higher frequency of proactive supervision strategies, more forceful reactions to stop supervisee risk taking, and a trend toward improved watchfulness. Supervisees in the no instruction condition also engaged in more hazard interactions compared with those in the instruction condition. CONCLUSIONS: Explicitly informing older children to supervise younger siblings may reduce younger children's risk of injury when siblings are supervising.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".