Les patrons de conception: Représentation et mise en oeuvre
Bibliographic record
Abstract
Design patterns are models of solutions to specific design problems in precise contexts. Since their apparition, they have raised a lot of interest. Some studies have concentrated on the classification, comparison and implementation of patterns, others have tried to specify formally patterns and/or their application. In this report we review several works that have studied the representation of patterns and the automation of their application while integrating them in development tools or environments. Resume Les patrons de conception sont des modeles de solution a des problemes specifiques de conception dans des contextes precis. Depuis leur apparition, ils ont suscite beaucoup d’interet. Certaines etudes se sont concentrees sur la classification, la comparaison et la mise en œuvre des patrons, d’autres ont essaye de specifier formellement les patrons et/ou leur application. Dans ce rapport nous passons en revue plusieurs travaux qui se sont interesses a la representation des patrons et a l’automatisation de leur application en les integrant dans des outils ou environnements de developpement.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 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".