Bibliographic record
Abstract
Every day a patient’s pocket becomes 425 dollars lighter for clinicians to guide and provide non-invasive prenatal testing. This research inquires: what are clinicians’ experiences of explaining prenatal screening and delivering genetic syndrome diagnoses? This paper Departs Radically in Academic Writing (DRAW) and presents key findings from qualitative interviews with 12 clinicians in “poetic scenes”, inspired by cultural theorist Lauren Berlant and anthropologist Kathleen Stewart. I describe DRAW, the birth story of a research rationale, and my metaphorical meeting with Berlant and Stewart. Then the poetic scenes begin: we enter doctors’ offices and bump into assumptions; we become a time-poor clinician, labouring in language construction with varying degrees of consciousness; we dissect “risk” – an ambiguous specimen; and we board the wrong train, going to prenatal screening destinations that we don’t like to name. We imagine a world where prenatal screening is built with poetry. We dream of attention to words.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".