‘I'm happy to own my implicit biases’: Public encounters with the implicit association test
Bibliographic record
Abstract
The implicit association test (IAT) and concept of implicit bias have significantly influenced the scientific, institutional, and public discourse on racial prejudice. In spite of this, there has been little investigation of how ordinary people make sense of the IAT and the bias it claims to measure. This article examines the public understanding of this research through a discourse analysis of reactions to the IAT and implicit bias in the news media. It demonstrates the ways in which readers interpreted, related to, and negotiated the claims of IAT science in relation to socially shared and historically embedded concerns and identities. IAT science was discredited in accounts that evoked discourses about the marginality of academic preoccupations, and helped to position test-takers as targets of an oppressive political correctness and psychologists as liberally biased. Alternatively, the IAT was understood to have revealed widely and deeply held biases towards racialized others, eliciting accounts that took the form of psychomoral confessionals. Such admissions of bias helped to constitute moral identities for readers that were firmly positioned against racial bias. Our findings are discussed in terms of their implications for using the IAT in prejudice reduction interventions, and communicating to the public about implicit bias.
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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 | 0.037 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.015 |
| 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".