Bibliographic record
Abstract
Epidemiology, like any branch of medical science, functions within a social and historical context. That context influences what questions are asked, how they are investigated, and how their conclusions are interpreted, both by researchers and by the public. The international debate over whether abortion increases breast cancer risk, which has been the subject of many studies and much heated controversy in recent decades, became so intensely politicized in the United States that it serves as a particularly stark illustration of how elusive the quest for scientific certainty can be. Although a growing interest in reproductive factors and breast cancer risk developed after the Second World War, it was not until the early 1980s, after induced abortion had been legalized in many countries, that studies began to focus on this specific factor. In the US these were the years following Roe v Wade, when anti-abortionists mounted their counterattack and pro-choice forces were on the defensive. As a result, epidemiologists found themselves at the centre of a debate which had come to symbolize a deepening divide in American culture. This paper traces the history of the scientific investigation of the alleged abortion-breast cancer link, against the backdrop of what was increasingly termed an “epidemic” of breast cancer in the US. That history, in turn, is closely intertwined with the anti-abortion movement's efforts, following the violence of the early 1990s, to regain respectability through changing its tactics and rhetoric, which included the adoption of the “ABC link” as part of its new “women-centred” strategy.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".