Worldview Implications of Believing in Free Will and/or Determinism: Politics, Morality, and Punitiveness
Bibliographic record
Abstract
OBJECTIVE: We used the FAD-Plus to investigate the association of free will belief (FWB) with political orientation, moral attitudes, and punitiveness. Other goals included (a) confirming the independence of believing in free will and determinism and (b) contrasting scientific determinism with fatalistic determinism. METHOD: Three studies were conducted via online questionnaires. Studies 1 and 3 recruited undergraduate students: Study 1, N = 220, M(age) = 20.96; Study 3, N = 161, M(age) = 20.2. Study 2 participants were recruited from a broader community sample: N = 253, M(age) = 34.29. RESULTS: Studies 1 and 2 found that FWB is associated with traditional conservative attitudes, including authoritarianism, religiosity, and belief in a just world. Study 2 replicated this pattern but narrowed the religiosity link to the intrinsic style. In Study 3, FWB was associated with binding moral foundations and retributive punishment of hypothetical criminals. CONCLUSIONS: Belief in free will is associated with a conservative worldview, including such facets as authoritarianism, religiosity, punitiveness, and moralistic standards for judging self and others. The common element appears to be a strong sense of personal responsibility. Evidence for distinct correlates of scientific and fatalistic determinism reinforces the need for treating them separately.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".