The Rare Sides of Twin Research: Important to Remember/Twin Research Reviews: Representation of Self-Image; Twins With Kleine–Levin Syndrome; Heteropaternal Lemur Twins; Risk of Dental Caries/In the Media: High-Society Models; ‘Winkelevii’ Super Bowl Twins; Multiple Birth × Three; Twin Sister Surrogate; A Presidential Twin?
Bibliographic record
Abstract
This article explores some rare sides of twin research. The focus of this article is the sad plight of the Dionne quintuplets, born in Canada in 1934. However, several other studies belong in this category, such as Dr Josef Mengele's horrifying twin research conducted at the Auschwitz concentration camp, Dr John Money's misguided attempt to turn an accidentally castrated male twin into a female, Russian scientists' cruel medical study of conjoined female twins and Dr Peter Neubauer's secret project that tracked the development of separated twins. Reviews of current twin research span twins' representation of self-image, twins with Kleine-Levin Syndrome, heteropaternal twinning in lemurs and factors affecting risk of dental caries. Media coverage includes a pair of high-society models, a book about the 'Winkelevii' twins, Super Bowl twin teammates, a family with three sets of fraternal twins, a twin sister surrogate and a near presidential twin.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".