Moderators of test–retest reliability in implicit and explicit attitudes.
Bibliographic record
Abstract
> 35,000). Explicit attitudes had greater test-retest reliability than implicit attitudes, but each showed considerable heterogeneity across topics even when measured within a single study session. Analyses also included several candidate moderator variables, such as attitude certainty or familiarity. While results were not identical, the moderators associated with greater test-retest reliability for implicit and explicit attitudes exhibited more similarities than differences. Specifically, attitudes experienced as more distinctive, more relevant to one's self-concept, more certain, and more accessible had higher test-retest reliability for both forms of evaluation. Variation in short-term reliability for implicit and explicit attitudes was replicated in Study 2, and Study 3 revealed that topics low in short-term reliability were also lower in a longitudinal sample that completed attitude measures separated by several weeks. These results advance our understanding of each attitude construct and are consistent with a more dynamic relationship between an attitude and its measure, as even attitudes measured with high levels of conscious control could show remarkable short-term instability when assessed only minutes apart. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.047 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".