Separating Multiple Processes in Implicit Social Cognition: The Quad Model of Implicit Task Performance.
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
The authors argue that implicit measures of social cognition do not reflect only automatic processes but rather the joint contributions of multiple, qualitatively different processes. The quadruple process model proposed and tested in the present article quantitatively disentangles the influences of 4 distinct processes on implicit task performance: the likelihood that automatic bias is activated by a stimulus; that a correct response can be determined; that automatic bias is overcome; and that, in the absence of other information, a guessing bias drives responses. The stochastic and construct validity of the model is confirmed in 5 studies. The model is shown to provide a more nuanced and detailed understanding of the interplay of multiple processes in implicit task performance, including implicit measures of attitudes, prejudice, and stereotyping.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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