Bibliographic record
Abstract
Entrepreneurs work in highly uncertain conditions, depend on limited resources, and try to make sense of the market complexity with their limited information on the contextual environment. In the process, they set revenue generating and profit-making goals, acquire and develop needed resources, build capabilities and skills, and analyze competitors, their products and their market approach to secure competitive advantage. However, most entrepreneurs take actions, make decisions and commitments, and allocate resources without doing sufficient analysis of the competing choices that they have. With a better understanding of what strategy frameworks and tools are available out there, how strategies work, how these tools and systems can help design their business activities, create value, and compete in the marketplace, they can take necessary actions toward achieving their objectives. Whether the entrepreneurs’ objective is to create wealth or to tackle a social challenge, they should adopt strategy tools to maximize their chances of achieving set goals. Here, we delineated some of the strategic means that are essential for the development of an enterprise and attain market competitiveness. Knowledge-based entrepreneurship needs to have a different approach to the designing of business models as businesses in these sectors rely heavily on emerging technologies and in-depth technological and scientific knowledge. In this chapter, we provided a detailed view of knowledge-based social entrepreneurship, possible strategic approaches available to aspiring entrepreneurs, various widely used business models, and emerging concepts in the development of business models for knowledge-based social entrepreneurship.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".