Bibliographic record
Abstract
Part I treated the factors that bear on the willingness of individuals to try entrepreneurship. In part II, we recognise that sometimes individuals have limited opportunities to become entrepreneurs, because of difficulties raising sufficient finance to purchase the working capital, marketing services, initial living expenses and other miscellaneous requirements needed to establish a business. Most start-up finance in developed countries tends to be personal equity (‘self-finance’), i.e. finance supplied by the entrepreneurs themselves. For example, according to the Bank of England (2001), 60 per cent of start-up businesses in Britain use self-finance. The remaining funds are raised from external sources. According to the Bank, about 60 per cent of external finance is raised through debt-finance contracts (comprising overdrafts and term loans) followed by asset-based finance (e.g. leasing: around 20 per cent). A similar picture applies in the USA, where banks also issue most debt finance. Also important is family finance, at around 10 per cent of external finance on average, whereas venture capital (equity finance) tends to play only a very minor role for most entrepreneurs (between 1 and 3 per cent). This chapter focuses on the implications for entrepreneurship of raising debt finance. Chapter 6 deals with various other sources of finance. If lenders and entrepreneurs were both perfectly informed about all aspects of new entrepreneurial ventures, and if financial markets were flexible and competitive, then all ventures with positive net present value (npv) would be funded. Also, it would not matter if lenders or entrepreneurs undertook the ventures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".