If Nazi = Red, and Canadian = Red, does Red = Good or Bad? Testing the effects of valenced perceptual cues on Implicit Association Test performance
Bibliographic record
Abstract
After two decades of research on implicit social cognition, it has become clear many of the field's theories and practices need to be redressed. Addressed in the present paper are the assumptions and memetic ideas embedded in the language, theory, and measurement tools of so-called implicit social cognition, their historical and contemporary shortcomings, and solutions to avoid these shortcomings and improve scholarship in this field. Specifically, the present paper recommends researchers and theorists adopt more accurate and epistemically honest language in their work, and determine what measures of implicit social cognition measure through experimental research rather than through empirically unjustifiable assumptions. Contributing to this endeavour, the present paper includes an experiment testing whether performance on the Implicit Association Test (IAT) can be changed through the indirect activation of complex associations. Here it is demonstrated that performance on IAT appears to be unaffected by indirectly activated complex associations, ruling out a possible cognitive mechanism that could contribute to performance on the IAT.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".