Why Do Citizens Discount the Future? Public Opinion and the Timing of Policy Consequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is widely assumed that citizens are myopic, weighing policies’ short-term consequences more heavily than long-term outcomes. Yet no study of public opinion has directly examined whether or why the timing of future policy consequences shapes citizens’ policy attitudes. This article reports the results of an experiment designed to test for the presence and mechanisms of time-discounting in the mass public. The analysis yields evidence of significant discounting of delayed policy benefits and indicates that citizens’ policy bias towards the present derives in large part from uncertainty about the long term: uncertainty about both long-run processes of policy causation and long-term political commitments. There is, in contrast, little evidence that positive time-preferences (impatience) or consumption-smoothing are significant sources of myopic policy attitudes.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it