‘Serious’ factor—a relevant starting point for further debate: a response
Bibliographic record
Abstract
In this reply, we wish to defend our original position and address several of the points raised by two excellent responses. The first response (De Miguel Beriain) questions the relevance of the notion of 'serious' within the context of human germline genome modification (HGGM). We argue that the 'serious' factor is relevant and that there is a need for medical and social lenses to delineate the limits of acceptability and initial permissible applications of HGGM. In this way, 'serious' acts as a starting point for further discussions and debates on the acceptability of the potential clinical translation of HGGM. Therefore, there is a pressing need to clarify its scope, from a regulatory perspective, so as to prevent individuals from using HGGM for non-therapeutic or enhancement purposes. The second response (Kalsi) criticizes the narrow interpretation of the objectivist approach and the apparent bias towards material innovations when discussing the right to benefit from scientific advancements. As an in-depth discussion of the objectivist and constructivist approaches was beyond the scope of our original paper, we chose to focus on one specific objectivist account, one which focuses on biological and scientific facts. We agree, however, with the critique that material innovations should not be the sole focus of the right to benefit from scientific advancements, which also incorporates freedom of scientific research and access to scientific knowledge scientific freedom and knowledge, including the influence of these on ethical thinking and cultures.
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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 | 0.036 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.102 | 0.127 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".