Savigny’s theory of choice-of-law as a principle of ‘voluntary submission’
Bibliographic record
Abstract
This article offers an innovative understanding of Friedrich Carl von Savigny’s comprehensive choice-of-law theory. This theory has been generally misunderstood in academic literature in that it has most often been perceived as a kind of blind reference to the mysterious ‘universal seat formula.’ In contrast to this commonly perceived view, it will be argued that Savigny’s choice-of-law theory is fundamentally grounded in the single organizing principle of ‘voluntary submission’ as a reflection of the person’s choice. Furthermore, it will be argued that the proposed understanding of Savigny’s choice-of-law theory is not detached from the reality of American judicial practice but, in fact, reflects it. In particular, it will be suggested that Savigny’s approach provides the key to grasping the theoretical underpinnings of the most central element of the popular Second Restatement – the most-significant-relationship principle.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| 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".