Risk Assessment and Risk Distortion: Finding the Balance
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.
Bibliographic record
Abstract
Pregnancy and birth have been conceptualized as medically problematic, with all pregnant women considered at risk and in need of medical monitoring. Universal application of risk scoring and surveillance as preemptive strategies in an effort to reduce risk is now standard obstetric practice. Labeling women "high risk" can result in more unnecessary interventions and have negative psychologic sequelae. When perceived pregnancy risk is out of proportion to the real risk, and when risk management procedures are applied to all women with benefit for only a few, the use of technology in caring for pregnant women becomes normalized. A learned reliance on technology can diminish women's own authoritative knowledge of pregnancy and birth. This may also have the unintended consequence of contributing to birth fear, a phenomena becoming more widely recognized. Health care provider-patient communication about pregnancy risk can be presented in a manner that encourages informed compliance rather than informed choice. Evidence-based risk assessment is essential to providing optimal prenatal care. Using tools such as the Paling Palette can help health care providers present balanced and readily understood information about risk.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it