Limited and Limiting Knowledges: Talking to Clients about Prenatal Screening
Bibliographic record
Abstract
This paper explores reflections from ten Canadian clinicians (nurses, midwives, family physicians and obstetricians) regarding their responses to women who decline prenatal screening. Additionally, it explores selfreported provider reflections on the biases they may bring into communicating with their clients/patients about these screens. Prenatal screening, while most often understand as a positive set of practices designed to provide pregnant women with helpful information about their unborn babies, touches on some of our most deeply held social, political and ethic beliefs. In this paper, prenatal screening is situated within social contexts of risk, disability, eugenics, and informed choice. As highly medicalized societies develop ever more accurate technologies, to test earlier, more accurately, with less risk and less expense, it is argued that we must simultaneously push for broad social reflection and analysis of the values, morals and ethics embedded in these screens.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.033 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".