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Record W2026071214 · doi:10.1215/s12280-008-9053-6

Life After Experiences of Infertility Treatment: <i>Akirameru</i>—The First Step for Empowering

2008· article· en· W2026071214 on OpenAlexfundno aff
Azumi Tsuge

Bibliographic record

VenueEast Asian Science Technology and Society An International Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
FundersAlberta Innovates - Health Solutions
KeywordsInfertilityAlienationNarrativeNorm (philosophy)FertilityPsychologyMedicineSocial psychologyPolitical sciencePopulationPregnancyPhilosophyLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.343
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

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