Bibliographic record
Abstract
Why certain people choose to pursue an entrepreneurial career has been identified as one of the defining questions of entrepreneurship (Shane & Venkataraman, 2003). Recent studies of entrepreneurial career choice have focused on the role of 'entrepreneurial cognition' which incorporates the use of mental models, heuristic thinking, intuition and pattern recognition (Baron, 2004). Another important cognitive factor in career selection is self-regulation, which refers to setting goals and then self-directing cognition and behavior towards the achievement of those goals (Vancouver, 2000). Like a number of earlier studies, I explored entrepreneurial self-regulation and career choice in terms of self-efficacy (e.g. Forbes, 2005). However, I also investigated two other important self-regulatory constructs, known as regulatory pride (Higgins & Friedman, 2001) and metacognitive awareness (Schraw, 1994), which have not been studied previously in relation to entrepreneurship (Baron, 2004). The literature suggests that all three constructs are related to career choice in terms of goal-setting and pursuit. Therefore, the investigation of these additional aspects of self-regulation extends and deepens previous research into entrepreneurial career choice and cognition.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".