Contribution du persona à la résilience des systémes. Impact sur les interfaces utilisateur
Bibliographic record
Abstract
Les systèmes sociotechniques sont résilients quand ils ont la capacité pour faire face aux situations imprévues sans précédent, pour lesquelles la sécurité n’a pas été spécifiée. Les quatre fonctions de la résilience sont : éviter l’accident, résister à un accident, adapter, restaurer une situation viable et stable après un accident. Nous proposons d’adopter et adapter le concept de persona pour formuler les caractéristiques fonctionnelles et non fonctionnelles des opérateurs humains de systèmes complexes, ainsi les impacts du fonctionnement des organisations. Cela nous permet d’identifier des solutions de conception pour les fonctions « éviter » et « s’adapter ». Nous appliquons la démarche à deux accidents de train en proposant ces solutions de conception sur l’architecture système et les IHM. ABSTRACT. The sociotechnical systems are resilient when they have the capacity to face the unprecedented unforeseen situations, for which the safety was not specified. The four functions of the resilience are: to avoid the accident, to resist to an accident, to adapt, and to restore a viable and stable situation after an accident. We suggest adopting and adapting the concept of persona to formulate the functional and not functional characteristics of the human operators of complex systems, so the impacts of the functioning of organizations. It allows us to identify design solutions for the following functions: "to avoid" and "to adapt ". We apply this approach to two railways accidents by proposing these solutions of design on the system architecture and the HCI.
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 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".