Bibliographic record
Abstract
The advance of medical technology now permits many genetic tests to be administered to a fetus in the womb. The goal of this testing is to determine the potential for genetically based disorders and disabilities. The use of these tests has major implications on the decision of a parent to abort a child based on what information they find in the prospective child's genes. Advocates of prenatal testing argue that it enables the families of these prospective children to make an informed decision when faced with the possibility of disability. I argue that this choice is drastically limited by social coercion through a discriminatory and stereotyped perception of the disabled community. Permitting an uncontrolled barrage of prenatal genetic tests will further promote the stereotype of a disabled life, and thus hinders our societal goal to recognise and promote equality and individuality. Which disabilities to test for, or what genes to search for, is a judgement that should be made only through extensive consultation with members of the disabled community, including individuals who have suffered from or who have been directly associated with the disability which is said to be tested.
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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| 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".