Twin Data that Made a Big Difference, and that Deserve to be Better-Known and Used in Teaching
Bibliographic record
Abstract
Because of their efficiency and ability to keep many other factors constant, twin studies have a special appeal for investigators. Just as with any teaching dataset, a “matched-sets” dataset used to illustrate a statistical model should be compelling, still relevant, and valid. Indeed, such a “model dataset” should meet the same tests for worthiness that news organization editors impose on their journalists: are the data new? Are they true? Do they matter? This article introduces and shares a twin dataset that meets, to a large extent, these criteria. In fact, while more than two decades old, the data are still widely cited today in ongoing related research. This dataset was the basis of a clever study that confirmed an inspired hunch, changed the way pregnancies in HIV-positive mothers are managed, and led to reductions in the rates of maternal-to-child transmission of HIV.
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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 | 0.047 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".