Être petit et réussir à l’export : étude de dix cas d’entreprises vitivinicoles françaises
Bibliographic record
Abstract
Plusieurs controverses jalonnent les recherches antérieures sur l’exportation des PME. L’objet de notre travail est d’analyser les facteurs de succès et les risques d’échec à l’international de ces entreprises. Pour cela, nous avons mené une analyse empirique sur dix cas d’entreprises vitivinicoles françaises. Nos résultats montrent une forte diversité des trajectoires à l’export et des visions distinctes du succès et de l’échec à l’international. Trois stratégies non exclusives (partenariale, communication, ressources humaines) se dessinent. Loin d’être un handicap, la petite taille des entreprises vitivinicoles peut se traduire par des attributs compétitifs distinctifs (lien au terroir, spécialisation, image-histoire du producteur).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".