Bibliographic record
Abstract
In “Providing Genetic Testing Through the Private Sector: A View From Canada” decisions constituting a rational evaluation of genetic testing for public funding or availability through private purchase were roughly organized into successive thresholds. The first thresho ld required that a genetic test be assessed for its moral propriety. The morality of a genetic test may depend in large part on its accuracy, usefulness, social and psychological risks, yet most would agree that it is vital to assess genetic tests for their moral intent or nature prior to adoption into clinical practice, and before decisions are made related to public funding and private access. For instance, sex selection and testing for trivial genetic conditions or for genetic contributions to complex abilities such as intelligence might be ruled as morally inappropriate. What might constitute such a strong moral argument against a genetic test that it justifies a prohibition before the tests are even implemented and evaluated? The ethical arguments against genetics tests fall into four categories.
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 | 0.051 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.099 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.027 | 0.024 |
| 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".