Hierarchy-Enhancing Misinformation: Social Dominance Motives Are Uniquely Associated With Republicans’ Belief In and Sharing of Election-Related Misinformation
Bibliographic record
Abstract
The aftermath of the 2020 US Presidential election saw a deluge of election-related misinformation which falsely asserted that the election was “stolen” from Donald Trump. Since then a majority of Republicans have consistently expressed belief in this misinformation, despite no evidence for its veracity and its motivating role in the January 6th, 2021 attack on the US Capitol. Here we present evidence, using a repeated-measures design (N = 355) across a highly generalizable stimulus set, that Republicans’ support for 2020 US election-related misinformation and willingness to share it on social media are uniquely associated with social dominance motives, along with conspiracy mentality and party identification strength. We find little evidence that right-wing authoritarianism is associated with the belief in or sharing of election-related misinformation, and that cognitive reflectiveness is only associated with sharing, but not belief. We introduce the theoretical lens of Hierarchy-Enhancing Misinformation to interpret these findings, arguing that election-related misinformation is best understood as a functional mechanism by which group-based dominance hierarchies are socially and psychologically reinforced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".