Conception, réalisation et mise à l'essai de trousses de déploiement pour faciliter et accélérer l'implémentation de la norme ISO/CEI 20000 par les très petites structures
Bibliographic record
Abstract
Plusieurs groupes de travail de l'Organisation internationale de normalisation (ISO) se sont organises, depuis un certain nombre d'annees, pour mener a terme le principal mandat du comite ISO/IEC JTC 1/SC7 qui consiste a offrir aux organisations un ensemble de standards pour le domaine de l'ingenierie du logiciel et des systemes. Un de ces groupes, notamment le Working Group (WG) 24, a concu le concept de trousses de deploiement dans le cadre de ses travaux. Une trousse de deploiement est un ensemble d'artefacts visant a faciliter et a accelerer l'implantation de la norme ISO dans les tres petites structures en leur donnant des processus prets a etre utilises. Le principal objectif du projet relate dans le present article est de developper et de deployer un ensemble de guides et d'exemples destine aux tres petites structures (TPS), sous la forme de trousses de deploiement lui permettant ainsi de mettre en place un systeme de management de ses services TI, suivant les exigences de la norme ISO/CEI 20000, facile a comprendre et qui est assiste par un ensemble de gabarits. Sept trousses de deploiement, en support a la norme ISO/CEI 20000, ont ete developpees et mises a l'essai avec succes dans une TPS du Quebec.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".