The Structure, Governance and Performance of UK Venture Capital Trusts
Bibliographic record
Abstract
Venture Capital Trusts (VCTs) are publicly traded venture capital companies in the UK. Since their inception in 1995, 71 VCTs have been launched, collectively raising more than £1.4 billion (as at November 2002). Investors have tax incentives to contribute capital to VCTs; in exchange, VCTs agree to be governed by statute. In this paper we argue VCT statutory governance mechanisms are less efficient than contractual governance among private venture capital limited partnerships. In support of this view, the available evidence is suggestive that VCTs have underperformed relative to other types of venture capital funds in the UK. Despite this apparent underperformance, in 2002 the British Venture Capital Association (BVCA) lobbied for statutory changes to facilitate VCT fundraising efforts through the expansion of allowable tax-exempt contributions, among other things. The available evidence on VCTs to date, alongside similar evidence from a comparable type of tax-subsidised public venture capital fund in Canada, is suggestive that these changes are not justified. A significant amount of further empirical research on VCTs and related public venture capital schemes in the UK is warranted before legislative changes expanding the scope of VCTs are adopted.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".