To Observe and Protect? How Digital Rights Management Systems Threaten Privacy and What Policy Makers Should Do About it
Bibliographic record
Abstract
The author begins the chapter by distinguishing between technological protection measures (TPMs) and digital rights managements (DRMs) systems, examining how such technologies are used to enforce corporate copyright policies and express copyright permissions imposed by a DRM through a registration process that requires purchasers to hand over personal information. Given DRM's extraordinary surveillance capabilities, the author argues that anti-circumvention laws must contain express provisions and penalties to protect citizens from organizations using TPMs and DRMs to pirate personal information, engage in excessive monitoring, and preclude people from exercising their right to access and control personal information. The author presents the view that any law which protects surveillance technologies used to enforce copyright must also protect people's privacy. Such laws must contain express provisions and penalties that protect citizens from organizations using TPMs and DRMs to engage in excessive monitoring or the piracy of personal information. In determining an appropriate balance, the author introduces three public policy considerations: (i) the Anonymity Principle; (ii) Individual Access; and (iii) Freedom From Contract. The author concludes with three corollary recommendations: (i) include an express provision prohibiting the circumvention of privacy by TPM/DRM, notwithstanding license provisions to the contrary; (ii) include an express provision stipulating that a DRM licence is voidable when it violates privacy law; and (iii) include an express provision permitting the circumvention of TPM/DRM for personal information protection purposes. These recommendations provide a set of counter-measures necessary to offset the new powers and protections afforded to TPM and DRM.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.039 | 0.060 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".