Modelling repeated ordinal reports from multiple informants
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
Cross-informant associations tend to be low for reports of children’s behaviours at one point in time. The paper extends the literature on multiple informants using data from a well-known longitudinal study of Quebec, Canada, boys to show how to estimate associations between repeated teachers′ and self-reports of aggressive behaviour. These associations, for both level and change, are derived from multilevel models for repeated measures of variables best treated as ordered categories. The ordering is represented by sets of continuation ratios, change by linear and quadratic functions of age, and the multivariate models are estimated using penalized quasi-likelihood. The analyses also incorporate a risk variable: socio-economic status (SES). The correlations between estimates of the growth parameters for the two sets of reports tend to be rather small and smaller than the cross-informant associations for levels. SES is associated with levels of aggression, more so for teacher reports than for self-reports, but not with the decline in aggression with age.
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 | 0.001 | 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