The psychological advantage of unfalsifiability: The appeal of untestable religious and political ideologies.
Bibliographic record
Abstract
We propose that people may gain certain "offensive" and "defensive" advantages for their cherished belief systems (e.g., religious and political views) by including aspects of unfalsifiability in those belief systems, such that some aspects of the beliefs cannot be tested empirically and conclusively refuted. This may seem peculiar, irrational, or at least undesirable to many people because it is assumed that the primary purpose of a belief is to know objective truth. However, past research suggests that accuracy is only one psychological motivation among many, and falsifiability or testability may be less important when the purpose of a belief serves other psychological motives (e.g., to maintain one's worldviews, serve an identity). In Experiments 1 and 2 we demonstrate the "offensive" function of unfalsifiability: that it allows religious adherents to hold their beliefs with more conviction and political partisans to polarize and criticize their opponents more extremely. Next we demonstrate unfalsifiability's "defensive" function: When facts threaten their worldviews, religious participants frame specific reasons for their beliefs in more unfalsifiable terms (Experiment 3) and political partisans construe political issues as more unfalsifiable ("moral opinion") instead of falsifiable ("a matter of facts"; Experiment 4). We conclude by discussing how in a world where beliefs and ideas are becoming more easily testable by data, unfalsifiability might be an attractive aspect to include in one's belief systems, and how unfalsifiability may contribute to polarization, intractability, and the marginalization of science in public discourse.
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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 | 0.007 | 0.024 |
| 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 | 0.013 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".