Life After Experiences of Infertility Treatment: <i>Akirameru</i>—The First Step for Empowering
Bibliographic record
Abstract
Although there is some awareness of how women in infertility treatment have suffered physically and psychologically, it is a little known fact that there is a limit to the “cures” that can be achieved even with assisted reproductive technologies. Here, I describe how the existence of ART affects women's decision making about their lives. Through life histories of women who underwent infertility treatment, I explore the factors which cause their suffering and conflict—that they cannot give up on having children even though they want to give up—as follows: (1) The models of their ideal family which have been formed throughout their lives is ‘ordinary’ family; (2) they experienced the alienation from their own bodies in infertility treatment; (3) they are afraid that they deviate from the community norm because of infertility; (4) their narrative shows their suffering from infertility is caused by tense relationship in family and community. These factors make women in infertility belittle themselves. Through their life histories, I conclude that they need to be empowered if they want to akirameru (give up) having children after prolonged infertility treatment. To paraphrase, a woman who suffers from infertility and infertility treatment is empowered when she becomes unafraid to deviated from cultural norms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".