Bibliographic record
Abstract
Until the late 1960s, the authorities on abortion were for the most part men—politicians, clergy, lawyers, physicians, all of whom had an interest in regulating women’s bodies. Even today, when we hear women speak publicly about abortion, the voices are usually those of the leaders of women’s and abortion rights organizations, women who hold political office, and, on occasion, female physicians. We also hear quite frequently from spokeswomen for anti-abortion groups. Rarely, however, do we hear the voices of ordinary women—women whose lives have been in some way touched by abortion. Their thoughts typically owe more to human circumstance than to ideology, and without them, we run the risk of thinking and talking about the issue of abortion only in the abstract. \n \nWithout Apology seeks to address this issue by gathering the voices of activists, feminists, and scholars as well as abortion providers and clinic support staff alongside the stories of women whose experience with abortion is more personal. With the particular aim of moving beyond the polarizing rhetoric that has characterized the issue of abortion and reproductive justice for so long, Without Apology is an engrossing and arresting account that will promote both reflection and discussion.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".