Modele de methodologie de gestion des risques d'un projet d'innovation technologique en technologie de l'information evalue par des etudes de cas dans le cadre de projets de developpement, d'integration ou d'un projet de type mixte
Bibliographic record
Abstract
The main objective of this case study research is to develop a risk assessment approach for technology projects in information technology for development projects, integration projects or mixed projects. The method RAFTIN (Risk Assessment For Technical INnovation) proposed in this thesis is based on a risk assessment grid and is built using a structured technique and is developed to adapt to the specific needs of each enterprise and each project within an organization. The model presents a scalable implementation method depending of project type and the addition of complementary services to the method. In this thesis, the expression innovation means a new technological implementation for the organization. This may be a new system development technology, an implementation of new technological products or a combination of the two categories of technology within a single project (mixed project). The risk evaluation is based on explicit steps or activities that are embedded within the planning of the global project. This research project includes a number of cases studies carried out in industry.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".