Children’s sympathy and sensitivity to excluding economically disadvantaged peers.
Bibliographic record
Abstract
Economically disadvantaged children often lack the resources to purchase popular goods and participate in their preferred social groups' activities, making it difficult to fit in. Meanwhile, children from middle socioeconomic status (SES) families may have additional influence over whether low SES children are included in such groups. We examined how a middle SES sample of 333 4- and 8-year-olds felt and reasoned about excluding a child who is economically disadvantaged (i.e., a needy child) versus a child who attends another school (i.e., a less needy child). We also examined whether children's dispositional sympathy was associated with their negatively valenced moral emotions (NVMEs) after hypothetically excluding. Older children reported feeling more NVMEs for both targets of exclusion. Furthermore, unlike 4-year-olds, 8-year-olds differentiated between the targets of exclusion by reporting more NVMEs after excluding a child who is economically disadvantaged. Lastly, children's sympathy was positively associated with their NVMEs after excluding a child who is economically disadvantaged but not a child who attends another school. We conclude that with increasing sympathy and age, children likely become more sensitive to the needs of their disadvantaged peers-an effect with meaningful implications for improving peer relationships across socioeconomic spheres. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".