Adjusting for Group Size Effects in Peer Nomination Data
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Bibliographic record
Abstract
Abstract Adjusting nomination‐based sociometric and peer assessment scores for biases due to variations in group size has been a long‐standing concern for peer relations researchers. The techniques that have been typically used to make these adjustments (e.g., proportion and standardized scores) are known to have fundamental problems that limit their utility. This study introduces a regression‐based procedure that adjusts nomination‐based scores for variations in group size and compares it with the standardization and proportion procedures. Analyses were conducted on sociometric and peer assessment scores of 1594 fourth, fifth, and sixth graders from 63 classrooms. The advantages of the regression‐based procedure over standardization and proportion transformations are outlined. Implications for the accuracy and validity of nomination‐based measures and the research findings based on them are discussed.
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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 | 0.000 |
| 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