The "Proof" of Foreign Normative Facts Which Influence Domestic Rules
Bibliographic record
Abstract
This article concerns the ascertainment by judges of normative facts that emanate from within foreign legal orders and must be taken into consideration in the interpretation of domestic rules. The author proposes an analytical approach which is based on three ideas. First, judges must remain in control of the process aimed at ascertaining such facts. Because the interpretation of domestic rules is at stake, they cannot remain passive and rule solely on the basis of the information adduced by the parties, as they normally do while ascertaining the contents of foreign rules under a classic conflict of laws scenario. Second, foreign normative facts are often reasonably disputable, and when that is the case the parties must be afforded the opportunity to comment on whatever information the court intends to rely on while ascertaining the contents of such facts. Finally, the assistance of experts may be necessary in some cases, but full-fledged party-appointed expert testimony will rarely be a cost-effective option. Judges and parties should consider alternative options, such as the testimony of a court-appointed expert or written statements provided by party-appointed experts.
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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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".