Bibliographic record
Abstract
Abstract Theories of policy responsiveness assume that political decision-makers can rationally interpret information about voters’ likely reactions, but can we be sure of this? Political decision-makers face considerable time and information constraints, which are the optimal conditions for displaying decision-making biases—deviations from comprehensive rationality. Recent research has shown that when evaluating policies, political decision-makers display biases related to heuristics—cognitive rules of thumb that facilitate judgments and decision-making—when evaluating policies. It is thus likely that they also rely on heuristics in other situations, such as when forming judgments of voters’ likely reactions. But what types of heuristics do political decision-makers use in such judgments, and do these heuristics contribute to misjudgements of voters’ reactions? Existing research does not answer these crucial questions. To address this lacuna, we first present illustrative evidence of how biases related to heuristics contributed to misjudgements about voters’ reactions in two policy decisions by UK governments. Then, we use this evidence to develop a research agenda that aims to further our understanding of when political decision-makers rely on heuristics and the effects thereof. Such an agenda will contribute to the literature on policy responsiveness.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".