How political partisanship can shape memories and perceptions of identical protest events
Bibliographic record
Abstract
It is well-recognized that increasingly polarized American partisans subscribe to sharply diverging worldviews. Can partisanship influence Americans to view the world around them differently from one another? In the current research, we explored partisans' recollections of objective events that occurred during identical footage of a real protest. All participants viewed the same 87-second compilation of footage from a Women's March protest. Trump supporters (vs. others) recalled seeing a greater number of negative protest tactics and events (e.g., breaking windows, burning things), even though many of these events did not occur. False perceptions among Trump supporters, in turn, predicted beliefs that the protesters' tactics were extreme, ultimately accounting for greater opposition to the movement and its cause. Our findings point to the possibility of a feedback loop wherein partisanship underlies different perceptions of the exact same politically relevant event, which in turn may allow observers to cling more tightly to their original partisan stance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".