Repair as Research: How Copyright Impedes Learning About Devices
Bibliographic record
Abstract
Widespread computerization and ubiquitous smart devices have enabled software-based copyright governance to reach into new domains. Beyond their instrumental utility, those devices contain vast amounts of information in the form of software and technical know-how. Through copyright and anti-circumvention rules, however, this information can be cordoned off and confined to exclusive distribution channels, significantly constraining research. While copyright law traditionally conceives of research as the use of expressive works within institutional settings, we propose a broader conceptualization that embraces device research, including informal inquiries and DIY activities. Whether for the purposes of modification, repair, user innovation, or testing, device research involves analytical engagement with physical devices. With a particular focus on repair-related activities as a modality of device research, this Article considers product teardowns, reverse engineering, security research, and testing analyses. It then looks to case studies that exemplify the ways in which copyright can impede this type of research. In highlighting the conceptual overlap between the Right to Repair and Right to Research movements, we argue that a broader concept of research in copyright that includes device research could normatively reinforce and bolster support for a Right to Research in international copyright law.
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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 | 0.065 | 0.194 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.029 | 0.041 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".