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Record W4391389238 · doi:10.1177/1357034x231222546

Embroidering Infertility: Using Art to Reveal and Resist Technobiopower in In Vitro Fertilisation Experiences

2024· article· en· W4391389238 on OpenAlexaff
Rochelle Einboden, Brooke Wylie, Rachael Simons, Janice Gullick

Bibliographic record

VenueBody & Society · 2024
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsIn vitro fertilisationInfertilityAndrologyResistFertilisationArtReproductive technologyBiologyMedicinePregnancyCell biologyMaterials scienceEmbryoGeneticsCryopreservationNanotechnology

Abstract

fetched live from OpenAlex

In the Western context of delayed motherhood and declining fertility, an array of fertility enhancements have emerged. While bioethical debates and literature on the technological prowess of these enhancements proliferate, it is useful to explore the lived experience of women undergoing them. This article uses Tabitha Moses’ artwork Investment, a series of embroidered hospital gowns, as a vehicle to explore lived experience of women engaging with fertility enhancements, including in vitro fertilisation. This analysis challenges dominant biomedical and corporate discourses framing fertility enhancements as benign. Using constructs of patriarchy, biopower, biopolitics (Foucault), and technobiopower (Haraway), we identify how power over women’s bodies is extended through fertility enhancements. Haraway’s notion of figuration supports the analysis. Understood as a tropic melding of semiotic and material, the figuration of The Infertile Woman offers a way to explore discursive operations of power and amid them the creation of a new form of reproductive labour.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.036
Scholarly communication0.0060.005
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.336
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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