Coalitionality shapes moral elevation: evidence from the U.S. Black Lives Matter protest and counter-protest movements
Bibliographic record
Abstract
Witnessing altruistic behaviour can elicitmoral elevation, an emotion that motivates prosocial cooperation. This emotion is evoked more strongly when the observer anticipates that other people will be reciprocally cooperative. Coalitionality should therefore moderate feelings of elevation, as whether the observer shares the coalitional affiliation of those observed should influence the observer's assessment of the likelihood that the latter will cooperate with the observer. We examined this thesis in studies contemporaneous with the 2020 Black Lives Matter (BLM) protests. Although BLM protests were predominantly peaceful, they were depicted by conservative media as destructive and antisocial. In two large-scale, pre-registered online studies (totalN= 2172), political orientation strongly moderated feelings of state elevation elicited by a video of a peaceful BLM protest (Studies 1 and 2) or a peaceful Back the Blue (BtB) counter-protest (Study 2). Political conservatism predicted less elevation following the BLM video and more elevation following the BtB video. Elevation elicited by the BLM video correlated with preferences to defund police, whereas elevation elicited by the BtB video correlated with preferences to increase police funding. These findings extend prior work on elevation into the area of prosocial cooperation in the context of coalitional conflict.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".