User “Safer Harbor” from Statutory Damages: Remixing the DOC’s IP Task Force White Paper
Bibliographic record
Abstract
In Safe Harbor for the Innocent Infringer in the Digital Age (Safe Harbor), I argued that certain classes of direct innocent infringers of copyright—namely, accidental and mea culpa infringers—should be afforded safe harbor from liability in light of current accepted online practices of users deemed essential for the proper functioning and progress of the Internet and digital technology. I offered a statutory amendment to Section 512 of the Copyright Act, one that would apply specifically to direct users and protect them in ways similar to the protections currently available to online service providers. In this Article, I approach the same topic from the damages phase and argue that a user’s actual or constructive knowledge of a copyright holder’s rights should be a factor in determining whether the holder’s damages award should be limited to the currently discretionary minimum award. Knowledge could even serve to create a presumption of culpability during the damages phase after liability has been determined. However, notice of copyright alone should not serve as a complete bar to a defendant’s ability to assert an innocent infringement defense that triggers a minimum statutory damage award. This approach is fairer and more just, especially in light of the fact that copyright infringement is a strict liability offense and exposes even the ordinary, low-level infringer to damage awards that are often questioned by commentators and judges alike as egregious and, in some cases, unconstitutional. Accordingly, I argue that in lieu of—or in addition to—my user safe harbor proposal in Safe Harbor, Congress should adopt a more meaningful minimum statutory damage award under Section 504(c) for certain classesof noncommercial infringement and commercial infringement causing little or no economic harm. This proposal would apply in cases where liability is established and the use is not otherwise legally permitted or excused. It would reduce statutory damage awards for technically infringing uses that support progress because they are socially beneficial, technologically desirable, or both... In light of the Department of Commerce’s 2016 Internet Policy Task Force report on statutory damages and the Copyright Office Section 512 Roundtables on the same topic, I discuss the report’s findings, as well as Canada’s approach to user rights for illustrative purposes, against the backdrop of my own recommendations.
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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 | 0.049 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.058 | 0.032 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".