Log-linear distance models of homophily in small groups
Why this work is in the frame
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Bibliographic record
Abstract
This article demonstrates the innovative use of the log-linear distance model in the assessment of homophily in a set of small groups, such as the co-participants in a set of events. It traces the development of the application of the log-linear distance model to the study of homophily and reviews its recent use to assess the extent and structure of gender and age homophily in groups of criminal accomplices (‘co-offenders’). The transformations of the group membership data that are a prerequisite of the log-linear analysis, and the interpretation of the log-linear parameters, are explained in detail in order to make the approach accessible to potential users. Although the described applications are to gender and age homophily in groups of criminal accomplices, the method can be used to assess homophily on one or more variables in any small groups.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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