Temporal Stability of Implicit and Explicit Measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A common assumption about implicit measures is that they reflect early experiences, whereas explicit measures are assumed to reflect recent experiences. This assumption subsumes two distinct hypotheses: (a) Implicit measures are more resistant to situationally induced changes than explicit measures; (b) individual differences on implicit measures are more stable over time than individual differences on explicit measures. Although the first hypothesis has been the subject of numerous studies, the second hypothesis has received relatively little attention. The current research addressed the second hypothesis in two longitudinal studies that compared the temporal stability of individual differences on implicit and explicit measures in three content domains (self-concept, racial attitudes, political attitudes). In both studies, implicit measures showed significantly lower stability over time (weighted average r = .54) than conceptually corresponding explicit measures (weighted average r = .75), despite comparable estimates of internal consistency. Implications for theories of implicit social cognition and interpretations of implicit and explicit measures are discussed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it