Bibliographic record
Abstract
After decades of optimistic portrayals, there has been a shift in the way that the popular press represents genomic research. A skeptical view has become more common. The central reason for this pendulum swing away from popular support is the harsh truth that most genetic risk information just isn't that predictive. This reality has created a fascinating policy paradox. If, as many in the scientific community are now saying, genetic information is not the oracle of our future health as we were once led to believe, and if access does not, for most, cause harm, why regulate the area? Why worry about shoddy direct-to-consumer (DTC) genetic testing companies? One primary justification, and one endorsed by the recent Canadian College of Medical Geneticists (CCMG) Policy Statement on DTC Genetics Testing, is that information that is conveyed to the public about genetics via marketing and to those who access DTC tests should, at a minimum, be accurate.
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 | 0.078 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.039 | 0.043 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".