Ideological influences on governance and regulation: The comparative case of supreme courts
Bibliographic record
Abstract
Abstract A key influence on governance and regulation is the ideology of individual decisionmakers. However, certain branches of government – such as courts – while wielding wide ranging regulatory powers, are expected to do so with no attitudinal influence. We posit a dynamic response model to investigate attitudinal behavior in different national courts. Our ideological scores are estimated based on probability models that formalize the assumption that judicial decisions consist of ideological, strategic, and jurisprudential components. The Dynamic Comparative Attitudinal Measure estimates the attitudinal decisionmaking on the institution as a whole. Additionally, we estimate Ideological Ideal Point Preference for individual justices. Empirical results with original data for political and religious rights rulings in the Supreme Courts of the United States, Canada, India, the Philippines, and Israel corroborate the measures' validity. Future studies can utilize Ideological Ideal Point Preference and the Dynamic Comparative Attitudinal Measure to cover additional courts, legal spheres, and time frames, and to estimate government deference.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".