What Can Political Psychology Learn from Implicit Measures? Empirical Evidence and New Directions
Bibliographic record
Abstract
Implicit measures have become very popular in virtually all areas of basic and applied psychology. However, there are empirical and theoretical arguments that might raise doubts about their usefulness in research on political attitudes. Based on a review of relevant evidence, we argue that implicit measures can be useful to identify distal sources of political preferences in domains where self‐presentation may bias self‐reports (e.g., influence of racial attitudes on voting decisions). In addition, implicit measures of proximal political attitudes can contribute to the prediction of future political decisions by virtue of their capability to predict biases in the processing of decision‐relevant information (e.g., prediction of voting behavior of undecided voters). These conclusions are supported by research showing that implicit measures predict real‐world political behavior over and above explicit measures. The reviewed findings suggest that implicit measures may serve as a useful supplement to improve the prediction of election outcomes. Open questions and potential directions for future research are discussed.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".