Bibliographic record
Abstract
It is evident from the history of the world that clustering of economic activities (emergence of ancient civilisations, agricultural revolution, industrial revolutions, economic changes during the 2 world wars, and present day globalisation process) takes place in different parts of the world, which mainly depends on the favourable environmental factors. This is true even in the case of venture capital financing, globally as well as with in each region in a country too. Though the North American region is leading with 59 % of the amount raised and 35 % of the amount invested globally (GPE 2006), the share of Canada is only 0.91% and 0.51% respectively (GPE 2006). This clustering is even happening between the regions too, 41% of the investment is in Ontario, 39 % in Quebec, and 11 % in British Columbia, and remaining 8 % is towards other regions in 2005. This clustering has significance in shaping the pattern of regional economic development. This article tries to analyse this clustering of venture capital financing activity in Canada for a period of 13 years (1993-2005), and tries to find out the Venture Capital Development Index of different regions for the last three years (2003-2005). TOPICS:Private equity, developed, quantitative methods
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".