Anxious Uncertainty and Reactive Approach Motivation (RAM) for Religious, Idealistic, and Lifestyle Extremes
Bibliographic record
Abstract
Reactive Approach Motivation (RAM) theory proposes that the personal uncertainty arising from motivational conflict causes anxiety, and that anxiety draws people to extremes because extremes activate approach‐motivated states that automatically downregulate anxiety. Five new studies consolidate existing evidence for the RAM view of uncertainty‐related threats and reactive extremism. In Studies 1–3, religious, idealistic, and RAM reactions after agentic, communal, and mortality threats were most extreme when threat‐relevant goals had been implicitly primed to create motivational conflict. In Study 4 uncertainty predicted extreme reactions only if goal conflict had been experimentally manipulated. In Study 5 personal uncertainty uniquely predicted lifestyle extremes among undergraduates whose educational goals were conflicted by a labor disruption at their university. Results converge on the conclusion that uncertainty‐related threats cause defensively extreme RAM reactions only if they arouse personal uncertainty about active goals. Results suggest that policies and programs to support the prosocial and/or nonextreme goals, ideals, and identifications of at‐risk people would reduce their motivation for antisocial extremism.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".