Government treatment of stem cell research: Bringing it out of the shadows of assisted human reproductive technologies
Bibliographic record
Abstract
Government treatment of stem cell research has primarily confined the parameters of discussion and consideration of stem cell research to its relationship with assisted human reproductive technologies. This failure to treat the medical benefits of stem cell research as a separate subject has negatively impacted the comprehensiveness of debate on the subject. This approach may explain the inadequate attention that has been given to the following issues: differences in treatment between assisted human reproductive technologies and stem cell research; the prohibition on cloning; ambiguities in implementation of the legislative provisions; consent issues; the treatment of chimaeras; the role of the Canadian researcher in the scientific community; and considerations raised by technologies developed in other countries. My main contention is that stein cell research deserves to be addressed as a separate subject from assisted human reproductive technologies in addition to its current government treatment.
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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.017 | 0.019 |
| 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".