The Computerized Implicit Representation Test: Construct and incremental validity
Bibliographic record
Abstract
Discrepancies in mental representations between self-aspects and significant others are associated with depression, personality disorders, emotional reactivity, and interpersonal distress. The Computerized Implicit Representation Test (CIRT) is a novel measure developed to assess discrepancies in mental representations. Inpatient participants (N = 165) enrolled in a longitudinal study completed baseline CIRT ratings of similarity between self-aspects (actual-self, ideal-self, and ought-self) and between actual-self and significant others (mother, father, liked others, and disliked others). Based on the similarity ratings, multidimensional scaling was utilized to generate distances between key self- and other representations in three-dimensional space. Results of univariate linear regression analyses demonstrated that discrepancies (distances) between self-aspects, actual-self to others, and actual-self to mother were significantly associated with impulsive and self-destructive behaviors and/or lifetime anxiety disorders. Multivariate hierarchical linear regression models further indicated that three CIRT variables provided incremental validity above and beyond age, gender, and/or borderline personality disorder.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".