The prospective map: a new method for helping future entrepreneurs in expanding their initial business ideas
Bibliographic record
Abstract
Business plan preparation is still a major element of entrepreneurship education programmes. However, it would appear that students are often asked to complete the exercise without first having an interesting idea on which to build their plans. It is therefore reasonable to think that entrepreneurship courses should be more concerned with introducing students to the exploration of business ideas, which is a crucial step in the opportunity identification process. However, there is a significant lack of appropriate teaching tools for this purpose. This study aims to fill part of this gap by proposing a method that can be used to help fledgling entrepreneurs explore the possibilities offered by their ideas, before beginning the more rigorous process of preparing a business plan. The method in question was used with 12 potential entrepreneurs, who were then asked to comment on the method's utility, describe the changes it produced to their initial idea, and suggest any changes to the method itself. The results are presented and commented upon.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".