L' efficacité de la veille et l'intelligence stratégiques et son impact sur la performance de l'organisation : proposition et tests empiriques d'un modèle de mesure de l'efficacité de la veille et l'intelligence stratégique et de son impact sur la performance de l'organisation
Bibliographic record
Abstract
Effectiveness of Strategic Watch and Intelligence (SWI) is vital for organizations in an international context of instability, complexity and intense competition. The research process of the thesis, includes a phase of theoretical construction, a qualitative phase using case studies, a quantitative phase and a phase of empirical tests. The results of the field studies and the interviewing of surveillance experts allowed for the development of an instrument of measurement in the form of a questionnaire, in conformity with the principle of a quality approach. The tool groups together producers and users of the SWI around the same goal of continuous improvement. It allows us to elaborate a control panel which can be used to pilot surveillance activity and its impact on the performance of the organization. In the final phase of research, the measurement instrument thereby developed was tested with those responsible for SWI and those who use it in two large companies : Framatome ANP and IBM.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".