A factor analytic and psychometric examination of pathology of separation–individuation
Bibliographic record
Abstract
Two studies are described that attempt to determine if standard-scale-reduction techniques could yield a construct-valid diagnostic screen of pathology of separation-individuation for use in nonclinical university settings. In Study 1 (N = 210), a measure of pathology of separation-individuation (PATHSEP) was reduced successfully to a single, internally consistent factor, accounting for 36% of the variance. In Study 2 (N = 304), these items also coalesced around a single factor, accounting for 35% of the variance. Study 2 also showed that PATHSEP is correlated moderately and positively with indices of insecure attachment, with the Center for Epidemiological Studies-Depression Scale, and with indices of psychiatric symptomatology (Hopkins Symptom Checklist). PATHSEP also was associated with a poorer profile of adjustment to college. Males reported more pathology of separation-individuation than did females. Evidence supports the construct validity of a shortened version of PATHSEP. Directions for future research are noted.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".