very easy for sensitive medical information to be accessed and sold by unscrupulous individuals that had access to the DNA database. Not only that, but with the increasing use of DNA in medical situations, the absolute protection of results of analysis cannot be guaranteed against seizure by law enforcement agencies. An interesting aspect to refusing to give a sample voluntarily is that such refusal raises an air of suspicion. This in turn gives an aspect to proceedings in court which move from innocent until proved guilty towards guilty until exonerated by DNA. This is simply not how it should be. The more and more samples on the DNA database, the more likely it becomes that a chance match results in a wrongful conviction. As of early 2004, Canada and France have shown a greater degree of responsibility than the progressive belief of the Forensic Science Service that they can get away with any infringement of what is very personal information. What Canada and France have done is made it mandatory that samples from accused persons who are subsequently not charged or acquitted should not be retained. Even more sensible, if samples are taken from juveniles, even if convicted, they will be destroyed when the individual becomes a legal adult. As we in the UK seem to be marching towards institutional control of our most personal information, American law enforcement agencies are astonished that in the land of the free we seem to have no interest in a public debate of what might become not just control of information, but control of the individual. 2 Cloning as a legal issue
Bibliographic record
Abstract
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.030 |
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".