A Modern Copyright Framework for the Internet of Things (IoT): Intellectual Property Scholars' Joint Submission to the Canadian Government Consultation
Bibliographic record
Abstract
In response to the Canadian government consultation process on the modernization of the copyright framework launched in the summer 2021, we hereby present our analysis and recommendations concerning the interaction between copyright and the Internet of Things (IoT). The recommendations herein reflect the shared opinion of the intellectual property scholars who are signatories to this brief. They are informed by many combined decades of study, teaching, and practice in Canadian, US, and international intellectual property law.In what follows, we explain:•The importance of approaching the questions raised in the consultation with a firm commitment to maintaining the appropriate balance of rights and interests in Canada’s copyright system, within the broader framework of the Constitution;•That the modernization of the Copyright Act requires a careful examination of the copyright framework within larger observable trends of dominant positions in the marketplace and anti-competitive practices, of the extraction of big (personal) data, and of market and legal infrastructures’ heavy reliance on non-negotiated standard form contracts;-That the growing prevalence of the IoT shows more clearly than ever before why Technological Protection Measures (TPMs) need to be recalibrated in keeping with the objectives of copyright, the Constitution, property rights, and of promoting competitive markets.As such, we recommend:-To narrow the scope of the TPM prohibitions under the Copyright Act, whereby the circumvention of access controls or copy controls for non-copyright-infringing purposes would be lawful, with a non-exhaustive list of such purposes to provide greater legal certainty. The same treatment would apply to the dealing in TPM circumvention technology enabling the exercise of non-copyright-infringing purposes.In the alternative, the Copyright Act should be amended to:-Introduce a new exception that would confirm that the TPM provisions (and other relevant exclusive rights in the Copyright Act) do not apply to the right to repair, including for maintenance and diagnostics purposes.-Introduce a new exception to encourage follow-on innovation.-Additionally, just as copyright holders should not be allowed to contract out of exceptions to copyright infringement through non-negotiated standard form agreements, neither should they be allowed to opt out of exceptions to TPM prohibitions by contract.
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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 | 0.050 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.043 | 0.034 |
| Scholarly communication | 0.033 | 0.010 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.047 | 0.040 |
| Insufficient payload (model declined to judge) | 0.010 | 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".