A cross-country study on investment readiness. How can entrepreneurs increase attitude towards equity finance?
Bibliographic record
Abstract
The creation and growth of high quality Small and Medium-sized Enterprises (SMEs) contributes to improvements in productivity from which the whole economy can benefit. However, the presence of market imperfections create difficulties for SMEs seeking external sources of equity finance to support the early stages of their growth. From the supply side, this equity gap is created by high transaction costs, ongoing running costs, high risk and uncertainty, and lack of exit options. From the demand side, the entrepreneurs‘ limited understanding of equity instruments, poor intrinsic quality (and presentation) of business plans, and a reluctance to share/cede control reduce the attractiveness of SMEs to formal equity financiers. We term such SMEs as ̳not investment ready‘. In order to tackle this crucial component of the equity gap, a range of targeted measures have been designed to increase the level of investment readiness and thus help those SMEs that wish to attract equity finance. This report will review publicly funded (either in whole or in part) investment readiness schemes in different countries (the European Union, USA, Canada, Australia and New Zealand), with the aim of understanding: (a) how effective these schemes are, (b) the extent to which they have been evaluated, (c) the results of the evaluations (d) and what lessons can be learned in designing future UK policies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".