One of Us: Autocratic Leadership Undermines Leader Support Less When Leader and Follower Are Politically Aligned
Bibliographic record
Abstract
ABSTRACT Autocratic leadership is on the rise globally, even in many long‐standing democracies. While democratically elected leaders who violate democratic norms often lose public support, political allegiances can shape the extent to which citizens withdraw that support. Drawing on social identity theory and the transgression credit theory of leadership, we hypothesize that while group members typically prefer nonautocratic over autocratic political leaders, this preference may weaken when a leader's political orientation or party affiliation aligns with that of group members, and thus the leader is viewed as a member of the ingroup. We test this hypothesis in seven studies (total N = 6385) spanning three countries, a range of leader targets (e.g., UK political leadership, Justin Trudeau, the leaders of the major Canadian political parties, Donald Trump, and Hillary Clinton), among both liberals and conservatives, at multiple time points over the span of 6 years, and combining both original survey data (Studies 1–4) and secondary analyses of nationally representative panel data from the United States (Studies 5–7). In all studies, participants reported their political affiliation, rated how autocratic they perceive their political leaders (Studies 1–4) or how much they perceive their political leaders disrespect democracy (Studies 5–7), and then evaluated their political leaders. We find robust support for our hypothesis that political affiliation moderates the relationship between perceived autocratic leadership style and leader support. Autocratic leadership style or perceived disrespect for democracy was associated with less favorable leader evaluations, but this negative association was significantly weakened when the leader shared the participants' political affiliation. Thus, although perceiving a leader as autocratic tends to erode leader support, it does so to a significantly lesser extent when the leader represents one's political ingroup.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".