Internet, facteur de gains de productivité et de diversification dans les PME : caractérisation des contextes d’usage
Bibliographic record
Abstract
Notre recherche s’intéresse aux caractéristiques de PME ayant simultanément su atteindre des gains de productivité et mettre en œuvre des processus de diversification grâce à leurs usages d’Internet. Dans un environnement économique en évolution permanente, marqué par l’innovation et la concurrence, cette double capacité apparaît comme un élément important de la viabilité et de la pérennité des petites et moyennes structures pour lesquelles la diversification externe, pratiquée par les grandes organisations, correspond à une stratégie trop coûteuse et donc, dans la pratique, inaccessible. La capacité d’obtention de combinaisons de ces deux avantages concurrentiels grâce aux usages d’Internet et de ses technologies connexes est expliquée par une analyse statistique détaillée des usages de la technologie reconsidérés dans leurs contextes organisationnels et industriels.
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 | 0.005 |
| 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 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".