The Theoretical Case Against Criminalized Copyright Infringement in Canada
Bibliographic record
Abstract
Criminalized copyright infringement has existed in Canada for close to a century. It has continued to expand in scope and severity since its first appeared in the Copyright Act, 1921. As Canada approaches 2017’s scheduled review of the Copyright Act, the time has come to ask whether the criminalization of copyright and its enforcement is theoretically justifiable. Yet, Canadian scholarship on criminalized copyright infringement is particularly scarce; there is a noteworthy gap in the existing literature wherein no one has systematically argued against criminalized copyright infringement from a theoretical perspective. This thesis aims to fill that gap, setting out a systematic legal and theoretical argument that criminalized copyright infringement, whether for personal use or financial gain, cannot be theoretically justified. In the absence of theoretical justification, the Government should move to decriminalize copyright enforcement.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.026 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".