Interpretation & The Internet, 28 J. Marshall J. Computer & Info. L. 251 (2010)
Bibliographic record
Abstract
Twenty years after the advent of the Internet, the revolutionary nature of the technology can no longer be in doubt. In spite of the ‘differentness” of the Internet, courts have proven adept at adapting extant law to the features and demands of this new technology. This paper will chronicle the differences between the Internet and other technologies which might, depending on the legal issue, justify the exclusion of the Internet from established rules on the basis of analogical reasoning. Two approaches to legal interpretation – literalism and purposivism—will be discussed in light of this new technology, with an explanation as to why a purposive approach to legal interpretation is the best guard against inappropriate application of a rule to new technology. Finally, a new methodology to provide courts with a template for how to approach interpretation of the law and the Internet will be discussed. This methodology will then be illustrated within the context of SOCAN v. Canadian Association of Internet Providers.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.019 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".