Bibliographic record
Abstract
Abstract Prospect theory has been adopted unevenly across different domains of political decision-making. Research drawing on prospect theory has contributed to important advances in the understanding of processes of elite decision-making in foreign policy and domestic politics. Political scientists have also contributed several important extensions of and qualifications to prospect theory that augment the original theoretical framework and are applicable in other disciplines. The next wave of research needs to be far more careful in specifying the scope conditions that have been the focus of research in behavioral economics. Scholars will also have to pay closer attention to the distribution of probability estimates across options; whether political decision makers are choosing among risky/certain bimodal distributions, high-probability distributions, high/low distributions, or low-probability distributions matters to the predicted impact of framing effects. Finally, studies will need to pay greater attention to the information political decision makers are given and to the impact of group dynamics in political settings. Identifying the scope conditions of prospect theory in the context of political and policymaking processes over time can make a significant contribution to the explanation of both domestic and foreign policy decisions, fill a gap between individual-level choice and institutionally based outcomes, and provide a stronger behavioral foundation for understanding the dynamics of multiactor policy choice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".