Automatic Self Recriminations: Development and Validation of a Measure of Self-Condemnatory Internal Dialogue
Bibliographic record
Abstract
This article introduces a measure of self-condemnatory internal dialogue as an element of the relationship with the self: The Automatic Self-Recrimination Scale (ASRS). Using the construct validation approach to test construction, we describe the initial development of items and report on findings from a clinical and nonclinical sample showing the ASRS is best understood as a multidimensional measure of self-critical internal dialogue composed of one higher-order factor and four lower-order facets: Not Mattering, Self as Failure, Undeserving Self, and Loathsomeness. The overall scale and four subscales demonstrated acceptable internal consistency and test-retest reliability. Moreover, there was evidence of good convergent and incremental validity of the ASRS subscales with measures of perfectionism, self-criticism, and dysfunctional attitudes. Overall, the ASRS appears to be a reliable and valid measure of an automatic self-recriminatory internal dialogue.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".