A Summary of the American Bar Association’s (ABA) Jurisdiction in Cyberspace Project : “Achieving Legal and Business Order in Cyberspace : A Report on Global Jurisdiction Issues Created by the Internet”
Bibliographic record
Abstract
"Suite à plus de deux années de recherches, le projet de l’American Bar Association (ABA) concernant la juridiction du cyberespace, « Achieving Legal and Business Order in Cyberspace: A Report on Global Jurisdiction Issues Created by the Internet », a été publié dans l’édition du mois d’août 2000 de la revue juridique The Business Lawyer. Ce rapport poursuivait deux objectifs distincts : Tout d’abord, effectuer une analyse globale des complexités potentielles entourant les conflits juridictionnels découlant du commerce en ligne. Ensuite, élaborer une liste exhaustive des solutions pouvant être utilisées pour résoudre de tels conflits. Le présent article se veut un résumé concis et accessible des trois grandes sections du « Cyberspace Jurisdiction Report » : (1) les solutions proposées aux problèmes juridictionnels découlant des conflits résultant du commerce électronique ; (2) afin d’appuyer les solutions proposées : l’utilisation d’exemples d’occasions où la technologie a déjà, par le passé, causé la métamorphose de certains paradigmes juridictionnels; et (3) afin d’appuyer les solutions proposées : l’utilisation d’un contexte doctrinal."
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.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".