Bibliographic record
Abstract
The intersection of consumer rights and corporate control is exemplified in the battle over automotive repairability, where claims of cybersecurity risks challenge the Right to Repair movement. This study critically examines challenges to Massachusetts' Data Access Law, which sought to expand independent access to vehicle telematics data for purposes of diagnosis, maintenance, and repair. Through critical discourse analysis, the findings expose rhetorical strategies that prioritize corporate interests under the guise of safety. This research emphasizes the need for policy interventions that prioritize transparency and innovation and recognize that robust security and equitable access to repair can coexist. Diriger la narration: une analyse sur la manière dont la rhétorique de la cybersécurité est utilisée pour entraver le droit de réparer RésuméL'intersection des droits des consommateurs et du contrôle des entreprises est illustrée par le combat sur la réparabilité des véhicules automobiles, où les allégations de risques de cybersécurité remettent en question le mouvement au droit à la réparation. Cette étude examine de manière critique les enjeux de la loi sur l'accès aux données du Massachusetts, qui visait à étendre l'accès indépendant aux données télématiques des véhicules à des fins de diagnostic, d'entretien et de réparation. Grâce à une analyse critique du discours, les résultats exposent les stratégies rhétoriques qui privilégient les intérêts des entreprises sous prétexte de la sécurité. Cette recherche souligne la nécessité d'interventions politiques qui donnent la priorité à la transparence et à l'innovation et qui reconnaissent qu'une sécurité solide et un accès équitable à la réparation peuvent coexister.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".