Are You Ready to Meet Your Baby? Phenomenology, Pregnancy, and the Ultrasound
Bibliographic record
Abstract
Iris Marion Young’s classic paper on the phenomenology of pregnancy chronicles the alienating tendencies of technology-ridden maternal care, as the mother’s subjective knowledge of the pregnancy gets overridden by the objective knowledge provided by medical personnel and technological apparatuses. Following Fredrik Svenaeus, the authors argue that maternal care is not necessarily alienating by looking specifically at the proper attention paid by sonographers in maternal care when performing ultrasound examinations. Using Martin Heidegger’s philosophy as a theoretical lens, the authors argue that sonographers who cultivate technical mastery, build patient rapport, explain the process and significance of the ultrasound, and understand the patient’s world are able to provide excellent patient care. The authors utilize Hans-Georg Gadamer’s hermeneutics to show how sonographers can frame the ultrasound in a way that acknowledges both the subjective knowledge of the mother and the objective data obtained by the sonographer through the use of technology. Ultimately, the authors argue that the common practice of framing the ultrasound as the chance to “meet the baby” is inappropriate, as it exacerbates the tendency to regard objective knowledge as the only legitimate knowledge in medical contexts. They recommend a more balanced approach that elicits a fusion of horizons between the patient’s subjective knowledge and the objective data that is obtained by the sonographer via the ultrasound, thus respecting and bolstering patient autonomy.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".