Sigma Ventures: Evaluating an Early-stage Venture Capital Investment (A)
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
Sigma Ventures is a venture capital (VC) firm that invests in technology, intelligent manufacturing, healthcare, and consumer services companies in their early and growth stage. In late 2017 Li Yuan, Sigma Ventures' founder and managing partner, needed to decide whether a startup called Isolimit was worth investing in. If so, then Isolimit was to be valued. The case describes how Sigma Ventures assessed Isolimit's team, market, and technology and shows how Sigma used the venture capital method to evaluate its potential investment. Specifically, the case discusses three aspects of early-stage venture capital investments: (1) How should venture capital firms evaluate early-stage startups? (2) What is the logic of the venture capital method? (3) How should venture capital firms apply the venture capital method to determine the percentage stake they should receive in exchange for their investment?
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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