Les Immigrants, Boosters de Recherche et Développement dans les Entreprises? – L’expérience Canadienne
Bibliographic record
Abstract
Résumé: La littérature académique confirme la relation positive entre l’immigration et les activités d’innovation. Les immigrants au Canada peuvent contribuer à l’innovation en raison de leurs niveaux élevés d’éducation et de leur diversité socioculturelle. L’objectif de la présente étude est d’examiner l’impact de l’immigration sur l’input en recherche et développement (R&D) à l’échelle des entreprises depuis les années 2000. L’analyse s’appuie sur la Base de données canadienne sur la dynamique employeurs-employés. Les principaux résultats sont les suivants. (i) Les entreprises mixtes avec un mélange des propriétaires natifs et immigrants présentent une probabilité plus importante de participer à la R&D et une intensité plus forte de l’input en R&D que les autres entreprises. (ii) La diversité ethnoculturelle des propriétaires immigrants favorise significativement les activités de la R&D dans les entreprises. (iii) L’accumulation du capital humain des immigrants joue positivement sur la R&D. (iv) Les étudiants internationaux diplômés ont le plus fort effet positif sur la capacité d’innovation. Abstract: Academic literature validates the beneficial relationship between immigration and innovation activities. Immigrants in Canada can contribute to innovation due to their high levels of education and socio-cultural diversity. The aim of this study is to examine the impact of immigration on research and development (R&D) input at firm level since the 2000s. The analysis is based on the Canadian Employer-Employee Dynamics Database. The main findings are as follows. (i) Mixed firms with a mixture of native and immigrant ownership have a greater probability of participating in R&D and a higher R&D input intensity than other firms. (ii) Ethnocultural diversity of immigrant owners significantly favors R&D activities in firms. (iii) Immigrants’ human capital has a positive impact on R&D. (iv) International graduate students have the strongest positive effect on innovation capacity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".