Bibliographic record
Abstract
In North America, prenatal testing and genetic terminations are becoming clinically normalized. Yet despite this implied social acceptance, open discussions surrounding genetic terminations remain taboo and silenced. Women are socially isolated, their experiences kept secret, and their grief disenfranchised. The lack of social consensus regarding genetic terminations, the valorization of scientific knowledge, and the bioethical framing of the issue as a matter of personal choice and autonomy collectively serve to reify this silence. In many respects genetic screening offers a form of technological surveillance procuring security from the unwanted kind of child. Yet the manner in which 'the unwanted kind of child' is understood varies from context to context. While we carry with us the consequences of decisions made elsewhere, the institutionalized discourses upon which these decisions are made are not always so readily transportable. One must somehow reconcile 'the unwanted kind of child' of the biomedical model with 'the unwanted kind of child' who was to be a member of one's family. In this paper, my intention is not to engage in the broader debate surrounding prenatal testing and genetic terminations. Rather, I employ my clinical encounters with these practices to illustrate the absence of an ethical language that might do justice to the experiences such practices construct. The limitations of a bioethical discourse that remains abstracted from lived experience are discussed.
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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.085 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".