Bibliographic record
Abstract
The globalization of market is progressing steadily as barriers to the free flow of goods, services, and capital declined since the end of World War II.This trend results in substantial increase in foreign competitions, consequently it increases the needs for new ventures to better differentiate by other basis than cost and quality.The thesis at hand used a constructive methodology to design an improved entrepreneurial framework.The purpose of the research is to provide theoretical foundations for new ventures to better cope with business opportunities discovery.The core contribution of the research is the conceptual development through real-world application of the project space model, an element of the Innovation Engine described by Bailetti (2013), to help new technology venture to increase their chances of success in determining the right market niche.The research also tries to provide answers to the following research questions: i) is the innovation engine model suitable for a web--based new ventures, ii) how can the project community component of the innovation engine be adopted by new ventures, iii) what processes new ventures can used to better cope with opportunity discovery?I would like to thank my advisors Michael Weiss, Tony Bailetti, Daniel Blanchette, Jon Milne and Rajiv Muradia for their times, support, wisdom, and guidance.The patience of each has allowed me to pursue my research interests and comments have helped to me seek the big picture in my research and identify implications for my findings.Likewise
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".