Bibliographic record
Abstract
Historians often despair of their ability to write histories of child murder, because the crime was easy to commit and conceal. Even today, coroners can determine only in rare instances whether a deceased infant or newborn was suffocated or died of natural causes (Knight 1996: 441–44, 345–60). No reliable test can determine, once decomposition has begun, whether a deceased newborn ever took a breath; and suffocation, unlike strangulation, leaves no physical marks, unless excessive pressure is applied to the face or lips. A murderer needed but a few moments to smother a child and could claim that the child was stillborn, had been accidentally overlain, or had died from natural causes. Unwanted pregnancies could be kept from public notice with the help of family or friends, especially pregnancies that came to term in late winter or early spring, when expectant mothers could live quietly out of the public eye or stay wrapped in heavy clothing. In New England, a large proportion of suspected neonaticides—nearly a quarter—occurred in April or early May, “mud season” in the Yankee vernacular,when people emerged from their long winter “hibernation.”
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
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".