Le rôle de la relation conjugale dans l’abus et la négligence d’enfants : vers une étude écologique
Bibliographic record
Abstract
The phenomenon of child abuse and neglect has always existed in Western society. Yet, it is only recently that clinicians and researchers have taken a serious look at these problems. This ever growing interest has come about from not only the stunning number of cases identified each year, but also by the extent and gravity of consequences observed among children. Several professionals interested by the issue have put forward a great number of etiological factors to try to explain abuse and neglect within the family unit. However, when assessing the proposed theoretical models (psychiatric/psychological, sociological, systemic), it appears that certain simple factors can play a large role, for instance the history behind parental development, the quality of marital relations, the child-parent relation, the stress and the extent of the social network, but none of these can clearly differentiate abusive families from non abusive families. It seems however that these different explicative factors would be even more valuable if they were considered in interaction rather than taken individually. Inspired by the ecological framework proposed by Bronfenbrenner (1977, 1979), Belsky (1980, 1984) as well as Cicchetti and Rizley (1981) have also developed a model that simultaneously takes into account all of these factors and their interaction.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".