Shane Rattenbury, the Productivity Commission, and the Right to Repair: Intellectual Property, Consumer Rights, and Sustainable Development in Australia
Bibliographic record
Abstract
Refereed Article. Matthew Rimmer, 'Shane Rattenbury, the Productivity Commission, and the Right to Repair: Intellectual Property, Consumer Rights, and Sustainable Development in Australia' (2023) 37 (3) Berkeley Technology Law Journal 989-1056. This Article tells the story of the fight for the right to repair in Australia. It is intended to complement comparative research elsewhere, looking at the right to repair in the United States and Canada; the United Kingdom, Switzerland, and the European Union; and other jurisdictions, such as South Africa. Part II of this paper considers the politics of the right to repair in Australia. It explains how Australian Capital Territory (ACT) Attorney-General Shane Rattenbury has sparked a larger law reform inquiry by the Productivity Commission into the right to repair. It highlights how Australia is particularly promising in terms of law reform – due to an unusual consensus amongst the major political parties across the usual divides. Part III focuses on the debate over intellectual property and the right to repair in Australia, and the recommendations of the Productivity Commission. It argues that there needs to be more than just copyright law reform; there should be matching reforms in designs law, trade mark law, patent law, trade secrets, and data protection. Part IV considers the recommendations of the Productivity Commission regarding consumer law and competition policy. It highlights the need for further law enforcement action to protect the right to repair. Part V explores the discussion about the right to repair in the context of sustainable development–looking at submissions on e-waste, the circular economy, and sustainable development. It contends that there should be greater law reform in these areas (going well beyond the limited recommendations of the Productivity Commission in this area). Part VI concludes by noting that the Productivity Commission has asked for action in particular markets in respect of automobiles, agricultural machinery, and tablets. The Article calls for the Australian Parliament to go further and recognise a more broadly based right to repair. Such a recognition will require a holistic approach, involving reforms to intellectual property laws, consumer rights and competition policy, and regulation of the environment and sustainable development. It maintains that it is necessary that the jurisdiction of Australia keep pace on the right to repair with its comparative partners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".