The Law Bytes Podcast, Episode 169: Alissa Centivany and Anthony Rosborough on Repairing Canada’s Right to Repair
Bibliographic record
Abstract
First published on Michael Geist's blog:\nThe right to repair would seem like a political no-brainer: a policy designed to extend the life of devices and equipment and the ability to innovate for the benefit of consumers and the environment. Yet somehow copyright law has emerged as a barrier on that right, limiting access to repair guides and restricting the ability for everyone from farmers to video gamers to tinker with their systems. The government has pledged to address the issue and Bill C-244, a private members bill making its way through the House of Commons, would appear to be the way it plans to live up to that promise.\nAlissa Centivany, an assistant professor in the faculty of information and media studies at Western University and the principal investigator of a SSHRC-funded research project on the right to repair and Anthony Rosborough, who completing his doctoral thesis at the European University Institute in Florence and is set to take up a joint appointment in Law and Computer Science at Dalhousie University later this year, have been two of the most outspoken experts on this issue in Canada. They join me on the Law Bytes podcast to talk about why the time has come for government action, their experience before a House of Commons committee on the bill, and unpack some of the confusion arising from late breaking amendments.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".