Incitatifs fiscaux dédiés aux anges investisseurs: une analyse critique des programmes instaurés dans le monde
Bibliographic record
Abstract
Dans cet article, les auteurs montrent pourquoi et comment de très nombreux gouvernements ont mis en place des incitatifs fiscaux dédiés aux anges investisseurs, qui financent des entreprises émergentes avec lesquelles ils n'ont pas de lien de dépendance. Les auteurs étudient également les conditions qui devraient être remplies pour que de tels programmes soient efficaces. Ils utilisent ces conditions comme une grille d'analyse de programmes types américains, européens, asiatiques et canadiens. Ils tirent enfin les leçons de cet exercice. Ces enseignements pourront être utiles au Canada, où de tels programmes existent déjà dans plusieurs provinces et sont à l'étude dans plusieurs autres. Le document est complété par des annexes qui décrivent sommairement la cinquantaine de programmes mis en place au cours des années récentes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".