A Comparative Investigation of the Predictive Validity of Four Indirect Measures of Bias and Prejudice
Bibliographic record
Abstract
Although measures of implicit associations are influential in the prejudice literature, comparative tests of the predictive power of these measures are lacking. A large-scale ( N > 100,000) analysis of four commonly used measures—the Implicit Association Test (IAT), Single-Category IAT (SC-IAT), evaluative priming task (EPT), and sorting paired features task (SPF)—across 10 intergroup domains and 250 outcomes found clear evidence for the superiority of the SC-IAT in predictive and incremental predictive validity. Follow-up analyses suggested that the SC-IAT benefited from an exclusive focus on associations toward stigmatized group members, as associations toward non-stigmatized group members diluted the predictive strength of relative measures like the IAT, SPF, and EPT. These results highlight how conclusions about predictive validity can vary drastically depending on the measure selected and reveal novel insights about the value of different measures when focusing on predictive than convergent validity.
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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 | 0.020 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".