Bibliographic record
Abstract
New developments in gene editing methods include the possibility to alter embryos for disease resistance. This could allow for increased immunity in the future, but at what cost? Gene editing may have unintended consequences. Some alterations may prevent the development of one disease but increase susceptibility to another. Other genes persist in populations for complex evolutionary reasons. Scientists must therefore consider the consequences and bioethics associated with these genetic changes. With examples such as the CCR5 coreceptor and major histocompatibility complex, it becomes clear that this type of genetic enhancement is immoral when evaluating it from biological, evolutionary, social, and economic perspectives. First, having the ability to select for certain desirable genes limits genetic diversity, which creates a barrier for evolution. Selecting for certain genes perpetuates the concept of ideal genes resembling dangerous eugenic ideologies. Should these procedures become more prevalent, the issue of accessibility arises. If these expensive procedures are only available to those who can afford them, the opportunity gap between the poor and the rich will widen. An investigation of case studies and ethical implications demonstrates that genomic editing is immoral and impermissible.
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 | 0.045 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".