Subtle primes of in-group and out-group affiliation change votes in a large scale field experiment
Bibliographic record
Abstract
Identifying the influence of social identity over how individuals evaluate and interact with others is difficult in observational settings, prompting scholars to utilize laboratory and field experiments. These often take place in highly artificial settings or, if in the field, ask subjects to make evaluations based on little information. Here we conducted a large-scale (N = 405,179) field experiment in a real-world high-information context to test the influence of social identity. We collaborated with a popular football live score app during its poll to determine the world's best football player for the 2017-2018 season. We randomly informed users of the nationality or team affiliation of players, as opposed to just providing their names, to prime in-group status. As a result of this subtle prime, we find strong evidence of in-group favoritism based on national identity. Priming the national identity of a player increased in-group voting by 3.6% compared to receiving no information about nationality. The effect of the national identity prime is greatest among individuals reporting having a strong national identity. In contrast, we do not find evidence of in-group favoritism based on team identity. Informing individuals of players' team affiliations had no significant effect compared to not receiving any information and the effect did not vary by strength of team identity. We also find evidence of out-group derogation. Priming that a player who used to play for a user's favorite team but now plays for a rival team reduces voting for that player by between 6.1 and 6.4%.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".