Bibliographic record
Abstract
Longtemps ignoree en France, l’hyperactivite fait maintenant l’objet de nombreux articles et emissions. Passe de « l’enfant mal eleve qui ne tient pas en place » au statut de veritable trouble, le TDA/H (Trouble du deficit de l’attention/hyperactivite-impulsivite) reste pourtant associe a de nombreuses idees recues : « L’hyperactivite est une mode qui vient des Etats-Unis », « C’est le resultat d’une education laxiste », « L’enfant hyperactif est incapable de se concentrer », « La Ritaline® est une drogue qui entraine des effets irreversibles », etc.Face aux difficultes des enfants et, souvent, au desarroi des parents, il est essentiel de poser un regard objectif, debarrasse des jugements hâtifs. C’est ce a quoi s’attachent Eric Acquaviva et Claudie Duhamel, en s’appuyant sur leur experience de praticiens, en detaillant et en illustrant de cas vecus les mecanismes et les enjeux de l’hyperactivite, ainsi que les moyens actuels pour la traiter. Eric Acquaviva est ancien interne et assistant-Chef de Clinique des Hopitaux de Paris en psychiatrie de l’enfant et de l’adolescent. Il exerce a l’hopital Robert Debre.Claudie Duhamel est docteure en psychologie, diplomee de l’universite du Quebec a Montreal (UQAM).
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".