An S-Curve Model of the Start-Up Life Cycle Through the Lens of Customer Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using the S-curve model of entrepreneurship, start-up funding, and customer development as a theoretical foundation, researchers can go in several directions. First, they can take a case study approach by investigating young start-ups and, using their financial statements, plotting performance longitudinally. Next, qualitative assessments can be done to understand potential risks that occur at each phase. Third, researchers can develop a greater understanding of the antecedents of early problems and what corrective actions can be implemented to curb the onset of trouble. Finally, large-scale quantitative assessments can be conducted to understand whether certain control variables, such as industry, culture, level of industrial development of the country, and experience of the entrepreneurs, can influence the stages in the S-curve model of entrepreneurship, start-up funding, and customer development. <b>TOPICS:</b>Private equity, analysis of individual factors/risk premia, statistical methods, equity portfolio management
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it