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Record W7046588950

DNA, Donor Offspring and Derivative Citizenship: Redefining\nParentage Under the Citizenship Act

2016· article· en· W7046588950 on OpenAlexaffabout

Bibliographic record

VenueeYLS (Yale Law School) · 2016
Typearticle
Languageen
FieldEngineering
TopicPulsed Power Technology Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsCitizenshipSperm donationImmigrationDisadvantageGovernment (linguistics)OffspringNaturalization
DOInot available

Abstract

fetched live from OpenAlex

Under Canada's Citizenship Act, children born outside Canada acquire derivative citizenship-that is, citizenship through descent or parentage-if at least one of their parents is Canadian. However according to Citizenship and Immigration Canada, in order to qualify for derivative citizenship a child must have a genetic link to a Canadian citizen. Canadians who use donated sperm or eggs to conceive-including women who give birth using donated eggs-are therefore not considered parents for citizenship purposes. According to the Federal Court of Appeal, Canadian donors may also pass on their citizenship to their genetic offspring. This article argues that current interpretations of the Citizenship Act disadvantage donor offspring and their families, and run counter to the Act's objectives, Parliament's intentions and developments in Canadian family law. It maintains that the Canadian government has provided inadequate justifications for excluding Canadians' non-biological children from obtaining citizenship by descent, particularly in light ofreforms permitting international adoptees to acquire citizenship from their Canadian adoptive parents. It recommends that citizenship officers be required to grant citizenship to donor offspring where Canadians are recognized as their parents for family law purposes, and can prove that their children were conceived using donated genetic material.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.586
Threshold uncertainty score0.832

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.035
Scholarly communication0.0070.003
Open science0.0020.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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.

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

Citations0
Published2016
Admission routes2
Has abstractyes

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