Comparative Biomedical Policy: Governing Assisted Reproductive Technologies
Bibliographic record
Abstract
Comparative Biomedical Policy: Governing Assisted Reproductive Technologies, Ivor Bleiklei, Malcolm L. Goggin and Christine Rothmayr, eds., London: Routledge, 2004, pp. 284. Few issues have the potential to combine elements of science fact and science fiction as does the field of biomedical research and its offshoot, assisted reproductive technologies (ART). As newspapers testify with regularity, this area of science and medicine uniquely combines promises for the improvement of human health but also exemplifies the dangers associated with scientists playing god with the very the building blocks of our species. Confronted with these stark opposites, public authorities have entered the fray and have tried, with varied responses, to frame these practices in such as way as to encourage and stimulate the positive elements of this area of research and medicine, such as in vitro fertilization, while cutting off or severely circumscribing the areas which have been deemed immoral or unethical, such as human cloning. It is where issues of morality or ethics enter the policy discourse that the waters get murky and where, as a result, governments find the arbitration between first-person experiences and societal norms the most difficult.
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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.024 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".