Evaluating the gap between research ethics review and data sharing in the pediatric infrastructure sciences: a case of big data and little ethics?
Bibliographic record
Abstract
BACKGROUND: Clinical progress in genomics-enabled learning health systems relies on the production, use and exchange of data, including from children.The policies and practices guiding proportionate governance of such production, access and exchange are, however, markedly limited in the pediatric genomics space.The need for policy-practice coherence in genomic data sharing can be accentuated when involving children, from whom data may require special protections.Absent understanding the ethical-legal bases upon which responsible pediatric data sharing rests, present and future children may not reap the benefits of a healthcare system that continuously 'learns' from the production, use and exchange of their data.The purpose of this thesis is twofold: to
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 | 0.393 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.032 | 0.118 |
| Scholarly communication | 0.038 | 0.040 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.021 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".