Infertility and moral luck: The politics of women blaming themselves for infertility
Bibliographic record
Abstract
Infertility can be an agonizing experience, especially for women. And, much of the agony has to do with luck: with how unlucky one is in being infertile, and in how much luck is involved in determining whether one can weather the storm of infertility and perhaps have a child in the end. We argue that bad luck associated with being infertile is often bad moral luck for women. The infertile woman often blames herself or is blamed by others for what is happening to her, even when she cannot control or prevent what is happening to her. She has simply had bad luck. We focus on the self-blame of infertile women and show how it stems from pro-natalism that targets women. We also argue that overall for women, regret is a better moral response to infertility than self-blame.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".