Sex selection, gender‐based violence and human rights abuse
Bibliographic record
Abstract
In the Peoples Republic of China over 10% of young women are missing. One major contributory factor to this loss of millions of human lives is sex selective abortion based on early ultrasound. This practice legal or illegal is also common in other countries such as parts of India. In this light it is surprising that Hsiao et al. in Acta number 1 2008 do not even mention this dilemma in their study of early fetal sex determination by ultrasound. They do refer to the fact that many Taiwanese/Chinese wish to have early information on fetal sex however they do not mention the ethical dilemma of disclosing such information particularly in parts of the world where such information may lead to - and does lead to - the earliest form of gender-based violence and major human rights abuse directed against the female sex. The Editors comment does raise this dilemma however this is insufficient as many readers will read the original paper only. FIGOs ethical guidelines emphasize that professional societies and their members are accountable for the employment of techniques for sex selection only for medical indications; and that in regional areas with marked sex ratio imbalance professional societies should work with governments to ensure that sex selection is strictly regulated. It is surprising to us that Acta would permit such a paper to go to print without any discussion in the paper of the ethical and human rights implications of the scientific findings. (full text)
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.022 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".