Methodological Issues in the Use of Peer Sociometric Nominations with Middle School Youth
Bibliographic record
Abstract
Studies reporting sociometric assessments based on nominations have been characterized by important methodological inconsistencies when conducted in the middle school context. The purpose of this study was to examine (1) the possibility of a response bias when participants are provided with a long roster sorted alphabetically, (2) the impact of including or not other-sex peers in the voting population, and (3) the impact of including or not all the grademates in the voting population. Participants were 664 sixth graders from three middle schools. Peer nominations for sociometric items (i.e., like most and like least), as well as teacher ratings of antisocial behavior and records of academic performance, were collected. A sequence effect in peer nominations was found, suggesting that students whose names were listed higher on the rosters received more nominations than did students whose names were listed lower on the list. Moreover, results indicated that the nominations received from the other-sex grademates and from the grademates outside the classroom improved the predictive validity of the sociometric measure. The implications of these results for the use of sociometric assessment in middle schools are discussed.
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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 | 0.359 | 0.519 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".