Encouraging entrepreneurship with innovation vouchers: Recent experience, lessons, and research directions
Bibliographic record
Abstract
Abstract Innovation vouchers are widely used internationally by governments to support emerging small business. Traditionally, vouchers were used to subsidize social benefits such as food, education or health services, but are increasingly used to stimulate entrepreneurial effort. Innovation vouchers are usually given to small firms to subsidize the cost of business or technical services from external providers. This enables the company to have more control over their development activities, while sustaining the external service providers. International and Canadian experience suggests considerable congruence in program design but, in some settings, special features have been devised to address local business needs and development priorities. A largely untapped body of evidence could be used to assess the impact of this tool and opportunities for refinement and application. Sommaire Les coupons pour l'innovation sont largement utilisés par les gouvernements à l'échelle internationale afin de soutenir les petites entreprises émergentes. Traditionnellement, les coupons étaient utilisés pour subventionner les avantages sociaux comme les repas, l'éducation ou les services de santé, mais ils servent de plus en plus à stimuler l'effort entrepreneurial. Les coupons pour l'innovation sont habituellement donnés à de petites entreprises afin de subventionner les coûts d'affaires ou les services techniques des fournisseurs externes. Cela permet à l'entreprise de mieux contrôler ses activités de développement, tout en maintenant les fournisseurs de services externes. L'expérience internationale et canadienne laisse entendre une remarquable congruence dans la conception des programmes, mais dans certains milieux, des caractéristiques spéciales ont été conçues pour répondre aux besoins des entreprises locales et aux priorités de développement. Un ensemble de données disponibles essentiellement non exploité pourrait être utilisé pour évaluer l'impact de cet outil et les possibilités d'amélioration et d'application.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".