Comment mener un exercice de couverture : Outil d'évaluation rapide de programmes et services
Bibliographic record
Abstract
Un exercice de couverture (EC) est un instrument d’évaluation simple, économique et rapide pouvant servir à profiler les personnes touchées par un prestataire donné, un groupe de prestataires ou des organisations partageant une clientèle commune dans une zone géographique particulière. Cet instrument a été mis au point pour assister les programmes de jeunesse, mais il peut également être utilisé par d’autres bénéficiaires ayant des services offerts en établissement ou en antenne. L’EC collecte les données relatives à différentes caractéristiques, y compris le sexe, la scolarisation, l’habitat, l’activité rémunératrice et l’état matrimonial des personnes bénéficiant d’un programme ou d’un service. Il permet au personnel et aux responsables des programmes d’évaluer systématiquement les services qu’ils fournissent, l’endroit exact de leur prestation et les caractéristiques de leurs bénéficiaires (clients habituels ou non). Le but ultime de l’instrument est de déterminer : (1) si les services atteignent leurs bénéficiaires prévus, et (2) s’ils sont appropriés et conviennent aux personnes qui les reçoivent. --- A coverage exercise (CE) is a simple, cost-effective, and rapid-assessment tool that can be used to profile the people reached by a given provider, group of providers, or organizations sharing a common customer base in a particular geographic area. This instrument was developed to assist youth programs, but it can also be used by other facilities that offer services and their branches. The CE collects data on different characteristics, including gender, educational attainment, habitat, income-earning activity, and marital status of people receiving a program or service. It allows staff and program managers to systematically assess the services they provide, the exact location of their delivery, and the characteristics of their beneficiaries (regular and nonregular clients). The ultimate goal of the instrument is to determine: (1) whether the services are reaching their intended beneficiaries, and (2) whether they are appropriate and suitable for the people who receive them.
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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 | 0.174 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".