Caprice Versus Standardization in Venture Capital Decision Making
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
This study examined the criteria used by venture capitalists to evaluate business plans in order to make investment decisions. A literature survey revealed two competing theories: “espoused criteria” where evaluation decisions are based on what venture capitalists say are the decisive factors, versus the use of“known attributes” that successful ventures actually possess. Brunswik9s Lens Model from Social Judgment Theory guided an empirical investigation of several different evaluation methods based on information contained in 129 business plans submitted for venture capital over a three-year period. Data evaluation culminated in the comparison of the percentage of correct decisions (“hit rate”) for each method. We found that decisions based on the known attributes of successful ventures have significantly better hit rates than decisions made using espoused criteria. Discussion centered on the goal of achieving consistency in the conduct of venture analysis. Process standardization can aid in the achievement of consistency. Future research will both deepen and broaden insights.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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