Pages from a sociometric notebook: Reconsidering the effects of selective missingness
Bibliographic record
Abstract
The effects of selective missingness on the size of observed correlations between scores derived from peer assessment procedures were examined with a sample of 719 boys and girls drawn from 57 peer groups in seven schools in Montréal, Québec, Canada or Barranquilla, a city on the northern Caribbean coast of Colombia in Latin America. Peer groups (i.e., the boys or girls within in a school classroom) in which participation rates exceeded 90% were randomly assigned to either a “complete” or a “missing” group. In separate procedures, children whose scores placed them above the 20th percentile for their group were excluded from the “missing” groups on measures of passive withdrawal, popularity, and aggression. When the correlations observed with the “complete” groups were compared with the correlations observed with the “missing” groups, few differences were observed. These findings are discussed within the context of the effects of missing data on peer assessment techniques and the factors underlying the association between different peer assessment measures.
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.
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 | 0.046 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".