The science of stem cells: ethical, legal and social issues.
Bibliographic record
Abstract
Stem cells are exciting to physicians, scientists and patients because of their potential to develop into many different cell types, tissues and perhaps even organs that can possibly be used to treat large numbers of patients with a variety of diseases. Scientific research, while it is still at a very early stage, is developing rapidly and creating enormous challenges for ethicists and policy-makers, especially in relation to embryonic stem cells. An understanding of the scientific facts of stem cell science and technology per se, the embryology and the associated terminology is critically important to making ethically sound policy judgments. The facts, definitions and terminology are confusing and are liable to misuse by those who seek to further a particular position. In this paper we provide a concise overview of the important scientific facts related to embryology and embryonic stem cells and highlight some recent scientific developments that are salient for the purpose of understanding the ethical, legal and social issues that have arisen and will continue to arise and be debated.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".