“Cinderella effects” in lethal child abuse are genuine and large: A comment on Nobes et al. (2019).
Bibliographic record
Abstract
Nobes et al. (2019) combined novel analyses of homicide victimization of British preschool children with a critique of previous research reporting large Cinderella effects (excess risk to stepchildren) in this domain. Whereas Nobes and colleagues' empirical contribution is useful, the critique contains factual errors and misrepresentations of the literature in support of their conclusion that the magnitude of such effects has been greatly exaggerated. It has not, as I show by addressing Nobes et al.'s many misstatements and reviewing relevant literature that they ignored. Fatal baby batterings, in particular, have been found to exhibit Cinderella effects on the order of 100-fold or more in many studies in several countries, including Britain. Nobes et al.'s efforts to deny this reality are misguided. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.042 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.015 | 0.007 |
| Research integrity | 0.092 | 0.086 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".