Book Review: Transforming Law’s Family: The Legal Recognition of Planned Lesbian Motherhood by Fiona Kelly
Bibliographic record
Abstract
Can you tell me the story of how you came to be a mother?"With this inspired question, Fiona Kelly began her research interviews of lesbian mothers who participated in the project that gave rise to her important book, Transforming Law's Family: The Legal Recognition of Lesbian Parenthood.Kelly's focus on planned lesbian motherhood reminds us that the women in her study, as is usually the case with lesbians, did not find themselves pregnant as a result of encounters in the back seat of a car or other accidents.Parents by choice, not chance, the lesbian mothers who opened their lives and stories to her, do not have "law or biological assumptions to fall back on [and] must define for themselves what makes someone a parent and where the boundaries might lie." 1 Kelly's first question must have felt like a breath of fresh air to the mothers, with its express invitation, and implicit recognition that their path to motherhood would have been unique and nonnormative.Kelly organized her research around four focal questions: how the lesbian mothers define 'family'; how parenthood is understood and defined within lesbian families; the role of sperm donors within a lesbian family; and finally,
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".