Theorizing the behavioral state: Resolving the theory-practice paradox of policy sciences
Bibliographic record
Abstract
Traditionally, the policy sciences exhibited a paradoxical relationship to public behavior: arguing in theory that it was rational in a utilitarian sense and could be modelled as such while at the same time recognizing its irrational nature in practice without attempting to reconcile this contradiction. A recent behavioral turn among policy scholars has broken the discursive hegemony of traditional hedonic compliance-deterrence models, however, placing informal institutions such as norms, irrationalities and collective action at the center of the policy research agenda. To date there has been little theorizing of the implications of this turn for the policy-making nature of the state, as well as its extent and nature. Addressing these gaps we conduct a bibliometric review, which finds that the number of behaviorally-oriented articles on policy instruments have been increasing in number and relevance. This provides evidence of a behavioral turn in policy studies as well as documenting the emergence of a behavioral state, that is one which is more inclined to reconcile policy-making theory and practice by embracing the irrationalities of policy actors, through the creation of nudge and behavioral units across a wide range of domains, a shift in emphasis from the supply of policy to the demands of policy targets. However, the study shows the impact of this turn is geographically and sectorally uneven and will become more generalized in the future only if more states embrace this ‘turn’.
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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 | 0.060 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.089 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.012 |
| 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".