Moving Beyond Commercialization: Strategies to Maximize the Economic and Social Impact of Genomics Research Editor’s Preface
Bibliographic record
Abstract
hosted by Genome Canada to facilitate a dia-logue between federal policymakers and re-searchers exploring issues at the interface of genomics and its ethical, environmental, eco-nomic, legal and social aspects (or GE3LS). Overarching themes for the series and spe-cific topics are selected on the basis of their importance and timeliness, as well as the “ripeness ” of the underlying scholarship. Ac-cordingly, the first series focused on “Genetic Information, ” whereas in year two, attention shifted to “Translational Genomics.” At the core of these exchanges is the devel-opment of policy briefs that explore options to balance the promotion of science and tech-nology while respecting the many other con-siderations that affect the cultural, social or economic well-being of our society. Co-authors of the briefs are leaders in their field and are commissioned by Genome Canada to synthesize and translate current academic scholarship and policy documenta-tion into a range of policy options. The briefs also benefit from valuable input provided by invited commentators and a group of expert participants and other stakeholders convened at half-day events in Ottawa. Briefs are not intended to reflect the authors’ personal views, nor those of Genome Canada. Rather than advocating a unique recommen-dation, briefs attempt to establish a broader evidence base that can inform various policy-making needs at a time when emerging ge-nomic technologies across the life sciences stand to have a profound impact on Canada.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".