The Standard of Proof for Jurisdiction Clauses
Bibliographic record
Abstract
The purpose of this article is to consider the standard of proof courts should use when resolving disputes about jurisdiction clauses. We will focus on disputes regarding the existence of a jurisdiction clause, although the conclusions on this point could be extended to questions concerning the interpretation of a jurisdiction clause. We argue that the full civil standard of proof, the balance of probabilities, should be used, rather than a lower, less stringent standard. As a precursor to our analysis, we will address a preliminary issue, namely what system of law will be used to determine such disputes. This question is important since, from a logical and practical perspective, it must be answered first. It is also noteworthy because, as will be explained, courts may be inappropriately linking the approach adopted for determining the applicable law and the approach adopted for the standard of proof.
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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 | 0.106 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".