Familles recomposées : qu’avons-nous appris au fil des ans?
Bibliographic record
Abstract
La croissance importante du nombre de familles recomposées a amené les chercheurs à examiner de plus près cette nouvelle réalité familiale. Cet article tente de cerner l'état des connaissances actuelles dans ce domaine en présentant les résultats de plusieurs études empiriques. Les thèmes ayant le plus retenu l'attention des chercheurs sont abordés dans cette recension des écrits, dont les caractéristiques structurelles de ces familles, la satisfaction et l'ajustement des adultes, le rôle de beau-parent et les effets de la recomposition familiale sur les comportements, les attitudes et les sentiments des enfants. L'auteure tente finalement de faire ressortir de l'ensemble de ces résultats certains constats qu'il serait intéressant d'approfondir dans les recherches futures.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".