Survey Study of 43 Supreme Court Common Law Judges on the Use of Foreign Law in Constitutional Rights Cases
Bibliographic record
Abstract
This is a survey study of 43 judges from the British House of Lords, the Caribbean Court of Justice, the High Court of Australia, and the Supreme Courts of Ireland, India, Israel, South Africa, Canada, New Zealand, and the United States on the use of foreign law in constitutional rights cases. As the focus of attempts to both explain and justify the use of foreign law in constitutional discourse, the attitudes of apex judges are clearly at issue. The study aims to shed light on how common law judges view foreign law as a source of argument in constitutional rights matters, and how they "see" transnational sources. The data provide the basis for preliminary testing of globalist theory (associated with Anne-Marie Slaughter, Vicki Jackson and Chris McCrudden). More generally, they lend a practical insight to jurisprudential debates invoking the nature of judicial reasoning in appellate courts. We find that the conception of judges citing foreign law as a source of persuasive authority is of limited application. Citational opportunism and the aspiration to membership of an emerging international "guild" appear to be equally important strands in judicial attitudes towards foreign law. We argue that their presence is at odds with Ronald Dworkin's theory of legal objectivity, and revealed in a manner meeting his own methodological standard for attitudinal research.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".