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Record W3006501724

[Neonaticides in France: analysis of 357 cases identified in the press (1993-2012)].

2017· article· en· W3006501724 on OpenAlexaboutno aff
Laurence Simmat-Durand

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyAbandonment (legal)Quarter (Canadian coin)MedicinePediatricsGeographyPolitical scienceSociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Background and objectives: Despite easy access to contraception and child abandonment in France, neonaticides continue to occur and, although rare, are widely publicized. The objective of this study was to characterize neonaticides and their perpetrators over a twenty-year period in France based on cases reported in the press. Methods: 2,319 press articles describing the discovery of a newborn corpse in the regional and national press were extracted from electronic databases or other digital supports. A total of 357 neonaticides were described, corresponding to a mean annual rate of 2.34 per 100,000 births. Results: The mother was identified in 74% of cases. The corpse was usually discovered in the house or garden (35%, mostly in the rubbish bin and 6% in the freezer), but also in the wilds (31%). In almost one-quarter of cases, the mother had suffered a haemorrhage. Most neonates were killed by asphyxiation (35%), direct blows or being thrown out of a window (11%), or drowning (11%). Only 22% of neonates died without the mother’s intervention, due to lack of care or neglect. Marked regional disparities were observed, even after calculation of regional rates. The mothers responsible (230 women due to 19 multiple neonaticides) had a mean age of 27.8 years and half of them had at least one other living child. Conclusions: Media coverage of neonaticides and access to electronic databases provide an opportunity to describe a rare phenomenon, for which it is difficult to collect sufficient sample sizes to allow analysis of the perpetrators and court rulings.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.316
Teacher spread0.265 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

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