Bibliographic record
Abstract
To the Editor — Your January editorial 1 touches on the issue of whether the heritable genome editing experiment resulting in the claimed birth of genome-edited twin girls Lulu and Nana (pseudonyms) should have been published — and more specifically whether excerpts of data from them should have been republished. On the basis of information in the public domain — slides presented by Jiankui He at the Second International Summit on Human Genome Editing 2 in Hong Kong in late November 2018 and excerpts of an unpublished manuscript authored by Jiankui He and colleagues disclosed 3 in MIT Technology Review in early December 2019 — these experiments are widely considered both unscientific and unethical. This prompts two discrete questions: “Should unscientific research be published?” and “Should unethical research be published?”
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 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.029 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.425 | 0.419 |
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".