The assertion of judicial jurisdiction over cyber-torts. A comparative analysis
Bibliographic record
Abstract
espanolEl objetivo de este trabajo es analizar los argumentos juridicos utilizados por los jueces en el common law y en el derecho civil como tradiciones juridicas cuando tienen que resolver problemas sobre la determinacion de competencia judicial sobre Cyber-Torts. Aqui se analiza la teoria alrededor de jurisdiccion, competencia, Tort, responsabilidad civil extracontractual y Cyber- Torts. Tales conceptos se confrontan con casos resueltos en Colombia, Canada y ee.uu. Para concluir que las reglas tradicionales de procedimiento civil son suficientes para resolver conflictos de jurisdiccion y competencia sobre casos con componentes del Internet. EnglishThe aim of this paper is to analyze the legal reasoning used by judges in common law and civil law legal traditions when they have to solve problems about assertion of jurisdiction over CyberTorts cases. Here I discussed around the theory of jurisdiction, competence, Tort, non contractual liability and Cyber-Torts. These concepts are compared with cases decided in Colombia, Canada and the U.S. To conclude that the traditional rules of civil procedure are sufficient to resolve conflicts of jurisdiction over Interne-based cases.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".