Dépendance spatiale sur données de panel : application à la relation Brevets-R&D au niveau régional
Bibliographic record
Abstract
Cet article présente les estimations de la relation brevets-recherche et développement (R&D) au niveau des régions françaises sur la période 1991-1996, en utilisant un modèle à erreurs composées avec dépendance spatiale. L’approche analytique prend en compte les effets des externalités spatiales des activités de recherche causées par la proximité technologique ou géographique des régions. Le modèle est estimé par le maximum de vraisemblance. Les résultats empiriques montrent une influence significative des dépenses de R&D sur l’activité des dépôts de brevets affectés aux régions. Les externalités technologiques sont positives mais leur signification dépend du caractère fixe ou aléatoire des effets régionaux. Par contre, les externalités géographiques demeurent absentes.
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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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".