Bibliographic record
Abstract
Welcomed as liberation and dismissed as exploitation, egg freezing (oocyte cryopreservation) has rapidly become one of the most widely-discussed and influential new reproductive technologies of this century. In Freezing Fertility, Lucy van de Wiel takes us inside the world of fertility preservation—with its egg freezing parties, contested age limits, proactive anticipations and equity investments—and shows how the popularization of egg freezing has profound consequences for the way in which female fertility and reproductive aging are understood, commercialized and politicized. Beyond an individual reproductive choice for people who may want to have children later in life, Freezing Fertility explores how the rise of egg freezing also reveals broader cultural, political and economic negotiations about reproductive politics, gender inequities, age normativities and the financialization of healthcare. Van de Wiel investigates these issues by analyzing a wide range of sources—varying from sparkly online platforms to heart-breaking court cases and intimate autobiographical accounts—that are emblematic of each stage of the egg freezing procedure. By following the egg’s journey, Freezing Fertility examines how contemporary egg freezing practices both reflect broader social, regulatory and economic power asymmetries and repoliticize fertility and aging in ways that affect the public at large. In doing so, the book explores how the possibility of egg freezing shifts our relation to the beginning and end of life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.103 | 0.032 |
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".