Patentability of living organisms : legal and ethical aspects of the question
Bibliographic record
Abstract
Given the considerable advances in the field of biotechnology in the last decades, new issues of scientific, social, legal and ethical nature have been raised, particularly concerning inventions making use of living material, and their patentability. Notwithstanding some reluctance at the outset, most of patent offices as well as courts and tribunals in the United States, Canada and Europe have finally accepted patentability of living organisms. Oppositions are however numerous and, more than a criticism towards the patent system itself, it is genetic engineering that is put into question. Europe has recently regulated the legal protection of biotechnological inventions. Being a text of compromise, the Directive is already subject of controversies. The United States and Canada have not yet decided to explicitly legislate in this field. Some decisions taken in particular cases allow to determine the state of the question in these two countries. It is however not certain that they can be satisfied with an unregulated technology that raises so many moral questions. The question of the foremost importance concerns the research branch, as well as the use that will be done with inventions emerging from the biotechnology industry. Patent law being unable to prevent technological creations, it is above all the utilisation of it that will allow to retain the most beneficial inventions for humankind and its environment.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.017 | 0.014 |
| 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".