Aide multicritère à la réallocation des ressources au Centre de Protection de l'Enfance et de la Jeunesse du Bas‐St‐Laurent (CJEP‐01)
Bibliographic record
Abstract
Résumé Beaucoup d'organisations privées ou gouvernementales, qu'elles soient publiques ou parapubliques, sont confrontées à un problème de rationalisation de leurs ressources financières et humaines. Dans le cadre plus précis des services offerts par le CPEJ‐01, il semblait que seule une réallocation des ressources humaines entre les divers points de services pouvait permettre une telle rationalisation. Pour aborder cette problématique de réallocation des ressources, il fut proposé de scinder l'enveloppe budgétaire totale en deux parties. La première était déterminée par l'application de normes provinciales. La seconde était, pour sa part, allouée selon les besoins différentiels des populations respectives des centres sous‐régionaux et ce, en vertu d'un principe d'équité. Ce principe d'équité étant par nature měme multidimensionnel, une démarche d'aide multicritère à la décision fut mise de l'avant dans l'allocation de cette enveloppe. Dans cet article, nous présentons le modèle d'aide à la décision multicritère utilisé dans ce projet de réallocation des ressources et le cadre de gestion de cette démarche d'aide multicritère à la décision. Abstract Many private or public organizations have been faced with the problem of rationalizing their financial and human resources. To deal with the problem, the administrators at the Child and Youth Protection Centre of the Lower St. Lawrence (CPEJ‐01) decided to reallocate their human resources between the different service areas of the region. The total budgetary envelope was divided into two, the first determined on the basis of provincial norms, and the second allocated according to the differential needs of the subregional populations on the basis of an equity principle. Since this equity principle was multidimensional, a multicriteria decision aid model was employed. In this article we present the model and the management framework used in this reallocation of resources.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".