Bibliographic record
Abstract
ObjectivesExisting decision-making practices and processes for sharing linked data for research are not keeping pace with the data tsunami and technological advances. The objectives of this project were to review existing approaches to decision making and to make recommendations for better decision-making practices and processes. ApproachWe used a hypothetical research application to compare decision-making practices and processes for sharing linked health data for research in three jurisdictions, Western Australia, Manitoba and Scotland. to We considered the decision makers; the relevant law, policy, and guidelines; and the ethical review process to assess practice and process against metrics of good decision making - efficiency, transparency, accountability and community participation. An analysis of the similarities and differences identified common problems and challenges with existing decision-making processes. Recommendations on how to address these common problems were proposed. ResultsThere were significant similarities in the decision-making processes in the three jurisdictions. These included: formal application processes; a statutory basis for decision making; criteria for waiving consent including low risk, impracticality, necessity, and protection of privacy and confidentiality; and at least some community participation in decision making and research. The main areas where decision making could be improved were: Efficiency — the number of decision makers and duplication of the issues considered by different decision makers. Separation of decision making on governance criteria and ethics criteria Transparency and accountability Community involvement ConclusionThis project has identified several areas where decision-making about sharing linked data for research could be improved. Six internationally relevant recommendations for better decision-making were developed covering a range of issues from identifiability to community involvement.
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 | 0.259 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".