Is There a There There: Towards Greater Certainty for Internet Jurisdiction
Bibliographic record
Abstract
The unique challenge presented by the Internet is that compliance with local laws is rarely sufficient to assure a business that it has limited its exposure to legal risk. The paper identifies why the challenge of adequately accounting for the legal risk arising from Internet jurisdiction has been aggravated in recent years by the adoption of the Zippo legal framework, commonly referred to as the passive versus active test. The test provides parties with only limited guidance and often results in detrimental judicial decisions from a policy perspective. Given the inadequacies of the Zippo passive versus active test, the paper argues that it is now fitting to identify a more effective standard for determining when it is appropriate to assert jurisdiction in cases involving predominantly Internet-based contacts. The solution submitted in the paper is to move toward a targeting-based analysis. Unlike the Zippo approach, a targeting analysis would seek to identify the intentions of the parties and to assess the steps taken to either enter or avoid a particular jurisdiction. Targeting would also lessen the reliance on effects-based analysis, the source of considerable uncertainty since Internet-based activity can ordinarily be said to create some effects in most jurisdictions. To identify the appropriate criteria for a targeting test, the paper recommends returning to the core jurisdictional principle -- foreseeability. Foreseeability in the targeting context depends on three factors -- contracts, technology, and actual or implied knowledge.
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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 | 0.034 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.021 | 0.046 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".