Implicit and Explicit Evaluation: A Brief Review of the Associative–Propositional Evaluation Model
Bibliographic record
Abstract
Abstract A central theme in contemporary psychology is the distinction between implicit and explicit evaluation. Research has shown various dissociations between the two kinds of evaluations, including different antecedents, different consequences, and discrepant evaluations of the same object. The current article provides a brief review of the associative–propositional evaluation (APE) model, which accounts for these dissociations by conceptualizing implicit and explicit evaluations as the behavioral outcomes of two functionally distinct, yet mutually interacting, mental processes. Whereas implicit evaluations are assumed to be the outcome of associative processes, explicit evaluations are conceptualized as the outcome of propositional processes. Associative processes determine the activation of mental contents on the basis of feature similarity and spatiotemporal contiguity; propositional processes involve the validation of activated mental contents on the basis of cognitive consistency. The APE model includes specific assumptions about mutual interactions between the two processes, implying precise predictions about converging versus diverging patterns of implicit and explicit evaluation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".