Constructing contentious and noncontentious facts: How gynecology textbooks create certainty around pharma-contraceptive safety
Bibliographic record
Abstract
Using critical discourse analysis, we examine how seven popular gynecology textbooks use sociolinguistic devices to describe the health effects of pharma-contraception (intrauterine and hormonal methods). Though previous studies have noted that textbooks generally use neutral language, we find that gynecology textbooks differentially deployed linguistic devices, framing pharma-contraceptive benefits as certain and risks as doubtful. These discursive strategies transform pharma-contraceptive safety into fact. We expand on Latour and Woolgar's concept of noncontentious facts by showing how some facts that are taken for granted by the medical community still require discursive fortification to counter potential negative accusations from outside the profession. We call these contentious facts. Our findings suggest that a pro-pharma orientation exists in gynecology textbooks, which may influence physicians' understanding of pharmaceutical safety. As such, these texts may affect medical practice by normalizing pharma-contraceptives without full considerations of their risks.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".