Structural Limitations of Data Protection Legislation for the Learning Health System: Proposing a Lex Specialis for Longitudinal Biomedical Data Use
Bibliographic record
Abstract
Data protection legislation imposes presumptive limitations on the capacity of institutions to collect, use, and disclose personal information to safeguard individual interests such as informational self-determination and privacy. This legislation has shown itself ill-suited to the regulation of emergent data-driven activities in the health sector, including public health surveillance, biomedical research, and personalised medicine. These difficulties are accentuated in transnational and intersectoral efforts, due to the heightened legal compliance challenges that arise from the simultaneous application of multiple laws. To alleviate these difficulties, a novel legal paradigm of data stewardship is proposed. A model of longitudinal information stewardship in reliance on appropriate organisational structures, expert oversight, and technical safeguards is proposed as an alternative to data protection law. Such a model should be enacted through special purpose health-sector legislation to align individual privacy and the pro-social use of health-related personal information in the public interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.111 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.028 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 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".