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Record W4402932220 · doi:10.1080/08893675.2024.2407501

Poetic scenes of prenatal screening

2024· article· en· W4402932220 on OpenAlex
Emma Cooke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Poetry Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
FundersAustralian Research Council Centre of Excellence for Plant Success in Nature and AgricultureDown Syndrome Research Foundation
KeywordsPoetryPsychologyLiteraturePsychoanalysisMedicineArt

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.344
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it