Financement de l'innovation dans les nouvelles petites entreprises: nouveaux elements probants provenant du Canada
Bibliographic record
Abstract
Dans ce document, on examine les caracteristiques financieres des nouvelles petites entreprises. L'analyse sert a etablir un profil financier representatif des petites entreprises et a evaluer la mesure dans laquelle sont correlees l'utilisation proportionnelle de sources et d'instruments differents et les caracteristiques propres aux industries et aux entreprises. On se sert d'une variete de methodes pour etudier les relations entre la structure financiere, l'intensite de la recherche et du developpement (R.-D.) et l'innovation. Nos resultats laissent entendre que la relation entre la forte concentration d'expertise et la structure financiere est bidirectionnelle. Apres avoir neutralise une serie de variables aleatoires propres aux industries et aux entreprises, celles qui consacrent un pourcentage plus eleve de leurs depenses d'investissement a la R.-D affichent aussi des structures financieres faisant moins appel a l'emprunt. Inversement, l'existence meme des structures financieres faisant fortement appel a l'emprunt contribue a limiter les investissements dans la R.-D. Ces relations dependent toutefois du type d'emprunt dans la composition de l'actif. C'est la part de l'emprunt a long terme de l'actif total qu'on rattache de maniere negative a l'investissement dans le savoir.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".