Bibliographic record
Abstract
Infants are highly selective in their help to unfamiliar individuals. For example, they offer more help to partners who move synchronously with them rather than asynchronously and to partners who interact with them in a “nice” rather than “mean” manner. Infant-directed song and speech may also encourage infant helping by signaling caregiver quality. In the present study, we investigated the effect of infant-directed song and recitation on 14-month-old infants’ subsequent helpfulness and proximity-seeking in relation to unfamiliar performers. During a 2.5-minute exposure phase, infants sat on their caregiver’s lap opposite an experimenter who sang “The Ants Go Marching” (song condition), recited the lyrics (recitation condition), or remained silent while parents read them a book (baseline condition). After the exposure phase, infants participated in a series of helping tasks that necessitated the return of objects dropped “accidentally”. Infants in the song and recitation conditions helped more than those in the baseline condition, but their helping of singers was moderated by song familiarity. Specifically, the extent of help directed to singers correlated positively with song familiarity. Singing (and to some extent, recitation) also encouraged infants to seek proximity with the experimenter. The findings indicate that rhythmic song and recitation by an unfamiliar adult foster infant affiliative behavior, but familiar songs may have special social importance.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".