Bibliographic record
Abstract
This paper probes interpretation issues elicited by the impact of digital technologies and the Internet on copyright law. The purpose of the paper is to instill a coherent framework for analyzing copyright law when it encounters Internet or digital facts. In part one, I propose a methodology of statutory interpretation that helps suitably adapt statutory language to technological developments. In essence it is this: courts should examine the language of the operative provision in its statutory context and in light of its purpose. A contextual interpretation of a broadly conceived rule can reveal a legislative intention that certain kinds of activities or things are to be included, even with respect to unforeseen technologies. More importantly with respect to new technologies, courts must always be ready to recalibrate the purpose behind a rule, i.e., to rebalance the interests inherent (and in some cases external) to the rule in way that seems most reasonable all things considered.\nIn part two, I introduce the reader to the challenges posed by Internet technology in connection with the interpretation of copyright law. While courts tend to treat the Internet as functionally the same as other technologies in most instances and proceed to analogize the facts before them with precedent cases, this approach on its own is not particularly effective. Rather, courts must always be attuned to the purpose behind a rule to help ensure the most appropriate application. Moreover, courts need to be cognizant that the nature of the Internet presents both external and internal perspectives of the facts. When courts relate the purpose of a rule to Internet or digital facts, they need to take into account both perspectives to ensure an interpretation that appropriately balances inherent and, where applicable, affected interests.\nIn part three, I apply the analysis and above methodology to three cases to help illustrate my thesis. The Supreme Court of Canada’s analysis of the section 2.4 safe harbour is an excellent illustration of how courts accommodate interests inherent and external to the rule in question. The MP3 case, on the other hand, presents as an example of how the Federal Court of Appeal failed to apply a purposive analysis and thus came to an unconvincing interpretive result. Perhaps most intriguingly, the Robertson case is discussed as an example of how the legal issue in dispute dictates the perspective of the facts that is adopted.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".