Bibliographic record
Abstract
Recently, an agonizing twist intersecting predictive genetic tests and surrogacy contracts made news headlines in Canada. The intended parents, a couple from British Columbia, instructed the surrogate mother with whom they were working to undergo First Trimester Screening and Chorionic Villi Sampling (CVS), which revealed the fetus likely had Down syndrome. The parents directed the surrogate to terminate the fetus or they would abdicate their parental claim upon birth. This story raised numerous legal and ethical questions relating to the transferability of decision making for prenatal screening, diagnostic tests, and the connected decision of termination when additional interested parties – the intended parents – are involved in the medical decision-making relationship. First, although the surrogate is the patient, what are the implications when the intended parents' wishes influence or even dictate the relationship between her and her obstetrician and genetic counselor? Second, if the intended parents' level of authority reaches explicit demands, may the intended parents ethically and legally require the surrogate to waive her right to informed consent for prenatal screening and diagnosis?
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 | 0.037 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.095 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.021 | 0.033 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".