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Record W4214872297 · doi:10.3366/ajicl.2022.0396

Surrogacy Legislation and Kenya's ART Bill 2019: Reproductive Uhuru (Freedom) A Myth or a Reality for Infertile Citizens?

2022· article· en· W4214872297 on OpenAlexaff
James Henry Olobo-Lalobo

Bibliographic record

VenueAfrican Journal of International and Comparative Law · 2022
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsCReATe Fertility Centre
Fundersnot available
KeywordsLegislationKenyaLawCommissionPolitical science

Abstract

fetched live from OpenAlex

In 2014, Kenyan parliamentarian Odhiambo Millie MP tabled the Assisted Reproductive Technology (ART) Bill [2019] to regulate assisted reproduction. The Bill restricts surrogacy to married couples only, prohibits payment to surrogates and makes no provision for surrogacy services or its oversight. It is modelled on the United Kingdom's surrogacy laws, although this article confirms the UK's surrogacy laws were intended to discourage surrogacy in the first place, and a Law Commission review shall be published in 2022. In 2007, Thiankolu Muthomi called for Kenyan-designed ART legislation. Kenya's customary woman-to-woman marriage is examined as a taking-off point for technologising Kenya's surrogacy services. The woman-to-woman marriage was constitutionally protected in 2010 and embedded by the enactment of the Protection of Traditional Knowledge and Cultural Expressions Act No. 33 [2016] that promotes the right to cultural expression. This cultural reality should provide the launching pad for a more permissive and auditable surrogacy legislation in Kenya and transferability to sub-Saharan Africa burdened, with the exception of South Africa, by unregulated ART practice.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.088
GPT teacher head0.350
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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