The Right to Repair Doctrine and the Use of 3D Printing Technology in Canadian Patent Law
Bibliographic record
Abstract
3D printing technology is part of a new economic movement, termed the sharing economy, where consumers rely less on large corporations for supplying them with products. The technology allows consumers to bypass the traditional manufacturing process. Instead, consumers increasingly share and sell products to each other on online sharing platforms. Consumers can download digital copies of products and print them in the convenience of their homes. In addition, they can repair and modify these products to suit their needs. Canadian patent law permits the repair of a patent-protected item but prohibits its reconstruction. However, the line between repair and reconstruction is unclear, which can cause tensions between consumers and patent-holders. This article argues that consumers should be given an allencompassing right to repair and modify legally purchased goods for private purposes using 3D printing technology if the repair or modification is not shared with others for a profit. This would give consumers the freedom to share their designs for free while still protecting patent-protected items from piracy. On a broader scale, the proposed legal right would encourage the sharing economy and build positive relationships between consumers and patent-holders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".