Celebrity Politicians and Affiliative Motives
Bibliographic record
Abstract
Previous research has revealed a willingness to vote for celebrity political candidates when one has formed a parasocial connection with that candidate. Parasocial connections are perceived social connections between an individual and a media personality. Although previous published research on this effect has been limited to one celebrity politician (i.e., Donald Trump), in an exploratory study we found that the relationship between parasocial connection and political support generalizes to at least eight other celebrities across multiple celebrity occupations (e.g., comedian, singer, actor, athlete). This relationship is not explained by parasocial connections inducing attributions of greater leadership ability in celebrity candidates (e.g., greater prestige, dominance, competence, or warmth). Thus, forming parasocial bonds with a large number of people (through fame) might be an emerging avenue for acquiring status and power in politics, because media personalities can form parasocial connections with millions of people and, consequently, gain supporters. We posit that willingness to vote for celebrities is driven by a desire to satisfy affiliative motivations. Given that parasocial connections are experienced much like genuine social relationships, individuals might endorse celebrity leaders to affirm social bonds with them. Individuals are motivated to support and reward others who provide them with social connection. This is typically adaptive, given that most social relationships are reciprocal and support given is often returned in many forms. Further, in the case of leadership, placing close affiliates in positions of power likely enhances one’s own fitness by creating vicarious influence over the group. In this way, supporting celebrities vying for leadership might represent an evolutionary mismatch, in which a typically adaptive motivation to support social partners who are seeking leadership is misapplied to parasocial partners who are unlikely to return the favor. Leadership choices are often governed by rational evaluations in which followers select and continuously evaluate leaders based on traits and behaviors that are likely to enhance group performance. However, if individuals are supporting celebrities as leaders due to their affiliative qualities, the effect of a celebrity’s behavior on leadership endorsement should differ from that of typical politicians. Previous work has shown that friends are evaluated differently than leaders. People prefer impartially beneficent leaders, but they prefer friends who show partiality (Everett et al., 2018). In other words, people prefer for their friends to be partial to those close to them, but prefer impartial leaders. Hence, if celebrities receive leadership support through affiliative motivations rather than typical leadership evaluations and corresponding motivations, celebrities’ behavior that is diagnostic of partiality should differentially affect their perceived suitability as leaders.
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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 | 0.006 |
| 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) | 0.008 | 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".