Return of Research Results: General Principles and International Perspectives
Bibliographic record
Abstract
Five years ago, an article co-written by some of us (Joly and Simard) presented an emerging trend to disclose some individual genetic results to research participants within the international research community. At the time, ethical norms and scholarly publications on the return of results often did not distinguish between the return of research results in general and the return of unexpected results (also called incidental findings). Both technologies and research practices have evolved significantly. Today whole genome and exome sequencing are increasingly affordable and frequently used in genetic research. Because these techniques produce a vast amount of interpretable and non-interpretable data (i.e., data of unproven significance) about an individual, the issue of how to manage information generated by such technologies needs to be considered. However, the development of international ethical guidelines has not kept up with the rapid pace of technological progress. Indeed developments in genomic biobanking also challenge the duty to disclose research results.
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 | 0.154 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.128 |
| Scholarly communication | 0.033 | 0.030 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.029 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".