Inaugurating Rationalization: Three Field Studies Find Increased Rationalization When Anticipated Realities Become Current
Bibliographic record
Abstract
People will often rationalize the status quo, reconstruing it in an exaggeratedly positive light. They will even rationalize the status quo they anticipate, emphasizing the upsides and minimizing the downsides of sociopolitical realities they expect to take effect. Drawing on recent findings on the psychological triggers of rationalization, I present results from three field studies, one of which was preregistered, testing the hypothesis that an anticipated reality becoming current triggers an observable boost in people's rationalizations. San Franciscans rationalized a ban on plastic water bottles, Ontarians rationalized a targeted smoking ban, and Americans rationalized the presidency of Donald Trump, more in the days immediately after these realities became current compared with the days immediately before. Additional findings show evidence for a mechanism underlying these behaviors and rule out alternative accounts. These findings carry implications for scholarship on rationalization, for understanding protest behavior, and for policymakers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".