Measuring what counts: Development of a new four‐category measure of adult attachment
Bibliographic record
Abstract
Abstract Measurement issues have plagued attachment research over the past 30 years. Concerns range from limitations of the original paragraph measure (C. Hazan & P. R. Shaver, 1987), low reliability of continuous scales of Bartholomew's 4‐category measure, limited interpretation of the 2 dimensions of the Experience of Close Relationships and the Experience of Close Relationships‐Revised (ECR/ECR‐R; K. A. Brennan, C. L. Clark, & P. R. Shaver, 1998; R. C. Fraley, N. G. Waller, & K. A. Brennan, 2000), and time‐consuming coding of attachment interviews. In this article, a revision of the Relationship Scales Questionnaire (RSQ) is introduced. The new 4‐category scales were found to have improved internal consistency when compared with the original RSQ scales as well as moderate to high test–retest reliability and good construct validity, thereby providing an alternative measure for researchers who are interested in assessing the effects of the 4‐category model of attachment.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".