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Record W2970334012 · doi:10.24926/25730037.588

Uncommon Misconceptions: Holding Physicians Accountable for Insemination Fraud

2019· article· en· W2970334012 on OpenAlexaboutno aff
Jody Lyneé Madeira

Bibliographic record

VenueMinnesota journal of law & inequality · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsInseminationDonor inseminationBusinessMedicineArtificial inseminationAndrologyBiologyPregnancyGenetics

Abstract

fetched live from OpenAlex

Recently, international headlines announced that four separate OB/GYNS inseminated unsuspecting patients with their own sperm from the 1970s through early 1990s. Decades later, genetic testing would reveal their transgressions. Strangely, Drs. Norman Barwin of Ottawa, Canada; Donald Cline of Indianapolis, Indiana; Gerald Mortimer of Idaho Falls, Idaho; Ben Ramaley of Greenwich, Connecticut; and John Boyd Coates of Berlin, Vermont were not the first such offenders—in fact, according to a 1987 survey by the federal Office of Technology Assessment, approximately two percent of fertility doctors who responded had used their own sperm to inseminate patients. Cecil Jacobson was convicted of federal mail and wire fraud, travel fraud, and perjury in the mid-1990s. In Europe, Dr. Jan Karbaat (now deceased) allegedly used his own sperm to father at least twelve children (from eight to thirty-six years old, according to a 2017 New York Times article). Not surprisingly, this conduct landed all three physicians in legal hot water; Jacobson was convicted on federal charges for mail, travel, and wire fraud; Cline pled guilty to obstruction of justice for lying about his actions, and Barwin and Mortimer face civil suits.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.957
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.077
GPT teacher head0.416
Teacher spread0.339 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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