Bibliographic record
Abstract
Dans le cadre de cette contribution, nous proposons une analyse du processus de décision d'adoption d’une démarche qualité par le dirigeant de PME. L’approche cognitive que nous retenons met l’accent sur l’articulation entre vision stratégique et intention stratégique. Elle nous permet de faire ressortir la diversité des stratégies d’adoption d’une démarche qualité en PME. L’accent est mis sur deux stratégies dominantes : la stratégie proactive, dont le principal objectif est la recherche d’un avantage concurrentiel, et la stratégie réactive, qui apparaît comme une réponse à de nouvelles exigences ou au comportement de concurrents amorçant eux-mêmes une démarche qualité. Nous privilégions dans cet article l’assurance qualité. En effet, cette démarche qualité semble de plus en plus s’imposer dans les relations entre grandes entreprises et PME.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".