Angel Financing for Entrepreneurs: Early-Stage Funding for Long-Term Success
Bibliographic record
Abstract
Foreword by Honorable Lorrie Keating Heinemann. 1. Introduction. 2. The Basics About Angel Investors. 3. Private Equity Investing. 4. Understanding Your Funding Needs. 5. Looking for Angels and What Angels Are Looking For. 6. Preparing for Investors. 7. Making the Match. 8. The Investment Process. 9. After the Investment. Appendix 1.Glossary of Terms Related to Private Equity and Debt Financing. Appendix 2. Non-Disclosure Agreement (Mutual). Appendix 3. United States Securities And Exchange Commission Regulation D. Appendix 4. Angel Organizations in the United States and Canada. Appendix 5.Angel Organizations in Europe. Appendix 6. Due Diligence Checklist. Appendix 7. Due Diligence Interview Questions. Appendix 8. Convertible Promissory Note. Appendix 9. Angel-Entrepreneur Internet Matching Sites. Appendix 10. Experts' Profiles. References. Acknowledgments. About the Author. Index.
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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 | 0.001 | 0.003 |
| 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 | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.139 | 0.055 |
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".