The interchangeability of liking and friend nominations to measure peer acceptance and friendship
Bibliographic record
Abstract
Two studies examine the convergence between measures of friendship and measures of liking in the assessment of friendship and peer acceptance. In the first study, 551 (301 boys and 250 girls) Canadian primary school children (ages 8–11) nominated friends and liked-most classmates. In the second study, 282 (127 boys and 155 girls) US primary school children (ages 9–11) nominated friends and rated classmates on a sociometric preference scale. The results revealed considerable convergence in the assessment of friendship. Most first, second, and third ranked friends were also nominated and rated as liked-peers, suggesting that when measures of liking are used to identify friends, few top-ranked friendships are overlooked. There was less convergence in assessments of peer acceptance. Peer acceptance scores derived from friend nominations were more strongly correlated with peer acceptance scores derived from liking nominations than with those derived from sociometric preference ratings. We conclude that liking nominations accurately capture friendships, particularly best friendships. Friend nominations may be a suitable substitute for assessments of liking, but they are a poor substitute for assessments of sociometric preference.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".