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Record W2952010171 · doi:10.48550/arxiv.cs/0109012

Is There a There There: Towards Greater Certainty for Internet Jurisdiction

2001· preprint· en· W2952010171 on OpenAlex
Michael Geist

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueArXiv.org · 2001
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCertaintyThe InternetJurisdictionMedicineComputer scienceWorld Wide WebPolitical scienceLawPhilosophyEpistemology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.066
GPT teacher head0.281
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it