Bibliographic record
Abstract
In recent years, conflicts between software access restrictions and Right to Repair (R2R) legislation have become a growing concern for policymakers and repair advocates around the world. Consumers have come to increasingly depend on electronic devices that integrate sophisticated hardware and embedded software. When those devices break or require maintenance, owners often lack the software or software-based tools required to fix them. In some cases where replacement parts and information may be readily available, device software and software-integrated tools present a barrier to independent repair. In response, legislators in both the United States and the European Union have been enacting R2R laws designed to empower consumers and professional repairers with access to these resources to foster a circular economy and reduce electronics waste. At the same time, trade negotiators on both sides of the Atlantic have been concluding free trade agreements (FTAs) that include digital trade provisions that protect software source code and algorithms from inspection and disclosure by governments or access by third parties. These provisions, such as those found in the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), the US-Mexico-Canada Agreement (USMCA) and subsequent EU-led agreements, bar governments from requiring device manufacturers to transfer or disclose source code or algorithms as a condition for market access. Though to date these parallel policy developments have (for the most part) occurred in isolation from one another, this report examines their potential for interaction and future conflict as contemporary FTAs and R2R mandates with software disclosure obligations come into effect. These seemingly distinct legal and policy developments may come into conflict where, for example, R2R mandates explicitly or implicitly require manufacturers to transfer or provide access to source code or algorithms for the benefit of third-party repairers or consumers. Drawing from statutory texts, recent trade agreements, policy briefs, and media reports, the study assesses the importance of access to software tools for repair, analyses domestic R2R legislation in the United States and Europe, surveys source-code provisions in major agreements, evaluates potential conflicts, and offers recommendations for policy makers.
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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 | 0.023 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".