Can singular examples change implicit attitudes in the real-world?
Bibliographic record
Abstract
Implicit attitudes about social groups persist independently of explicit beliefs and can influence not only social behavior, but also medical and legal practices. Although examples presented in the laboratory can alter such implicit attitudes, it is unclear whether the same influence is exerted by real-world exemplars. Following the 2008 US election, Plant et al. reported that the Implicit Association Test or "IAT" revealed a decrease in negative implicit attitudes toward African-Americans. However, a large-scale study also employing the IAT found little evidence for a change in implicit attitudes pre- and post-election. Here we present evidence that the 2008 US election may have facilitated at least a temporary change in implicit racial attitudes in the US. Our results rely on the Affective Lexical Priming Score or "ALPS" and pre- and post-election measurements for both US and non-US participants. US students who, pre-election, exhibited negative associations with black faces, post-election showed positive associations with black faces. Canadian students pre- and post-election did not show a similar shift. To account for these findings, we posit that the socio-cognitive processes underlying ALPS are different from those underlying the IAT. Acknowledging that we cannot form a causal link between an intervening real-world event and laboratory-measured implicit attitudes, we speculate that our findings may be driven by the fact that the 2008 election campaign included extremely positive media coverage of President Obama and prominently featured his face in association with positive words-similar to the structure of ALPS. Even so, our real-world finding adds to the literature demonstrating the malleability of implicit attitudes and has implications for how we understand the socio-cognitive mechanisms underlying stereotypes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".