The Covariance Structure Model Analysis of the Factors affecting the Entrepreneur's Human Capital Pricing in Venture Capital
Bibliographic record
Abstract
This paper carries on the analysis of the factors affecting entrepreneur's human capital pricing in venture capital, focusing on the much-dimensionality characteristic, then establishes a logical and comprehensive theory frame which can reflect all the factors affecting entrepreneur's human capital pricing. Further by using covariance structure model (CSM), we make an empirical analysis of these factors. The result shows, in venture capital the control rights of entrepreneur is the most important factor affecting the entrepreneur's human capital pricing, the scale of enterprise is the least one, with the enterprise management performance in the middle. Key Words: venture capital, the entrepreneur's human capital pricing, factors, covariance structure model Resume: L’article present, sur la base de l’analyse des facteurs affectant la fixation du prix du capital humain de l’entrepreneur dans l’investissement-risque, a etabli pour ce probleme multi-dimensions un cadre theorique logique et capable de refleter completement ces facteurs influants. L’auteur adopte d’ailleurs la methode analytique du modele de la structure de covariance (CSM) pour effectuer une etude positiviste sur ces facteurs. Le resultat montre que, dans l’investissement-risque, le pouvoir de controle de l’entrepreneur est le premier facteur en jeu, la perfomance de l’entreprise le deuxieme et la dimension de l’entreprise le dernier. Mots-Cles: investissement-risque, fixation du prix du capital humain de l’entrepreneur, facteurs affectants
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".