Bibliographic record
Abstract
The largest percentage of individuals seeking tocreate new firms are between the ages of 25 and 35. Consequently, therole that aging plays in entrepreneurial behavior is examined.UsingBecker's theory of time allocation, a model is created toidentify athreshold age at which an individual's willingness to invest time in startingnew firms declines. The model makes the assumption that individuals distribute their timebetween income-producing activities and leisure.There is a particular agefor each individual at which the allocation between the income-producingactivities and leisure provides maximum expected utility. The model demonstrates that risk aversion and wealth are key factors in anindividual's decision to pursue a new venture.As individuals grow older,the discount associated with each dollar of future income increases.Inturn, those activities that require a time commitment before an income isproduced are not viewed as being as attractive as wage labor, where the returnis immediate. The model presented can be adapted to consider other factors that may bepertinent to an individual's decision to become an entrepreneur.(SRD)
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".