Bibliographic record
Abstract
Canada has one of the highest levels of venture capital investment as a share of GDP among OECD countries. Between 1995 and 2001, Canada realised phenomenal growth in venture capital supply and the creation of over 200 new venture capital funds. However, the largest share of Canadian venture capital goes to follow-on funding of smaller firms -- rather than to new deals involving start-ups -- and to traditional manufacturing sectors. In the late 1990s, the Canadian government began attempts to diversify the sources of venture funds through liberalising rules for institutional and foreign investors, modifying tax incentives and introducing government equity funds. Foreign investors, particularly from the United States, are now the major players and are targeting their funding to technology-based start-ups. This paper analyses trends in Canadian venture capital markets and makes policy recommendations which have been developed through an OECD peer review process ... Politiques de capital-risque au Canada ExprimA©s en pourcentage du PIB, les niveaux d’investissement en capital-risque du Canada sont parmi les plus A©levA©s des pays Membres de l’OCDE. Entre 1995 et 2001, la croissance de l’offre de capital-risque y a A©tA© phA©nomA©nale et plus de 200 fonds nouveaux de capital-risque se sont crA©A©s. Toutefois, l’essentiel du capital-risque canadien est consacrA© A la poursuite du financement de petites et moyennes entreprises (PME) – et non de nouveaux dossiers impliquant de jeunes entreprises – et aux secteurs manufacturiers traditionnels. A€ la fin des annA©es 1990, les pouvoirs publics canadiens se sont lancA©s dans des tentatives de diversification des sources de capital-risque en libA©ralisant la rA©glementation applicable aux investisseurs institutionnels et A©trangers, en modifiant les avantages fiscaux et en crA©ant des fonds publics de participation au capital. Les investisseurs A©trangers – amA©ricains notamment – sont maintenant les principaux acteurs du secteur ; leurs financements ciblent ...
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".