Smearing the opposition: Implicit and explicit stigmatization of the 2008 U.S. Presidential candidates and the current U.S. President.
Bibliographic record
Abstract
Four studies investigated whether political allegiance and salience of outgroup membership contribute to the phenomenon of acceptance of false, stigmatizing information (smears) about political candidates. Studies 1-3 were conducted in the month prior to the 2008 U.S. Presidential election and together demonstrated that pre-standing opposition to John McCain or Barack Obama, as well as the situational salience of differentiating social categories (i.e., for Obama, race; for McCain, age), contributed to the implicit activation and explicit endorsement of smearing labels (i.e., Obama is Muslim; McCain is senile). The influence of salient differentiating categories on smear acceptance was particularly pronounced among politically undecided individuals. Study 4 clarified that social category differences heighten smear acceptance, even if the salient category is semantically unrelated to the smearing label, showing that, approximately 1 year after the election, the salience of race amplified belief that Obama is a socialist among undecided people and McCain supporters. Taken together, these findings suggest that, at both implicit and explicit cognitive levels, social category differences and political allegiance contribute to acceptance of smears against political candidates.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".