Methodological Issues in the Use of Peer Sociometric Nominations with Middle School Youth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it