MétaCan
Menu
Back to cohort
Record W7070690476

Právní a etické aspekty anonymního dárcovství spermatu se zaměřením na právní řád Kanady

2016· dissertation· en· W7070690476 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2016
Typedissertation
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationContext (archaeology)PopularitySperm donationDonationEgg donationReproductive technologyAnonymityThe Right to PrivacyRight to know
DOInot available

Abstract

fetched live from OpenAlex

Nowadays, anonymous sperm donation is indeed a globally discussed topic: the popularity of the use of assisted reproduction technology for the purpose of conception is directly proportional to increasing infertility and technological progress. Also, the duration of its use already started to show, with the first generation of opinionated anonymous donor children growing up. The aim of my work is to analyze what are the today's Western society's current ethical and legal views on the anonymous sperm donation and its alternatives, as well as what they should be, and to demonstrate these on the example of Canada, United Kingdom and Australia. In the first two chapters, I outline the context of ethical and legal thinking about anonymous sperm donation: I analyze the concept and implications of the infertility, as well as the development and types of methods of assisted reproduction. Further, I proceed from the basis of ethical reasoning and the determination whether the right to know one's origins, implying the right to know the circumstances of one's conception, exist, and its competition with the rights of parents and donors in the third chapter, to its legal grounds. First, in the fourth chapter, I deal with legislation on the right to know one's origins and other related rights in the international...

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0030.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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.

Study designBench or experimental
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 routes1
Has abstractyes

Explore more

Same venueDigital Repository (National Repository of Grey Literature)Same topicQR Code Applications and TechnologiesFrench-language works237,207