State Resident Handedness, Ideology, and Political Party Preference: U.S. Presidential Election Outcomes Over the Past 60 Years
Bibliographic record
Abstract
Pearson correlation, partial correlation, and multiple regression strategies determined the degree to which estimates of the level of left-handedness in each of the 48 contiguous American states related to citizen political ideology and to Democratic-Republican presidential popular vote over the past 60 years. Higher state levels of left-handedness were associated significantly with liberal ideology in each of the presidential election years from 1964 to 2016. Comparable ideology data were not available for 2020. Higher state levels of left-handedness also were associated with a greater degree of Democratic candidate popular vote support in each of the presidential election years from 1964 to 2020 except for 1976. The mean size of these 28 significant Pearson correlations involving the two political criteria was .62 ( SD = .12) with a range of .38–.80, indicating handedness alone could account for a mean of 40.1% ( SD = 14.9) of the variance in the two political preference variables. Corresponding multiple regressions showed that when state-level Big Five personality, White population percent, urbanization, and income variables were given the opportunity to enter the equations, handedness still emerged with a significant regression coefficient in 26 of the 28 equations. The two exceptions occurred for 1968 with either political preference criterion. It is speculated that such relations are grounded in hypothesized but poorly understood genetic links between handedness, personality, and political beliefs and attitudes, and, that a foundational genetic predisposition to left-handedness in a population may have much greater impact on correlates than overt levels of left-handedness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".