An Investigation on Knowledge-Based Entrepreneurship in Higher Education
Bibliographic record
Abstract
Purpose: This section provides an overview of the article's main objective and scope. It introduces the concept of knowledge-based entrepreneurship in higher education institutions and sets the stage for the research's exploration of its various aspects, including drivers, challenges, and transformative impacts. Theoretical Framework: This section discusses the theoretical underpinnings and concepts that guide the research. It mentions how universities have evolved from traditional knowledge disseminators to hubs for entrepreneurial initiatives and outlines the theoretical foundation upon which the study is built. Methodology: This part briefly outlines the research methodology employed in the study. It mentions the systematic analysis of existing literature as the primary research approach and highlights the study's focus on knowledge transfer, collaborative relationships, and the role of Technology Transfer Offices (TTOs). Findings: This section summarizes the key findings of the research. It touches upon the pivotal role of universities in the knowledge economy, the mechanisms of knowledge transfer, and the significance of TTOs in facilitating knowledge-based entrepreneurship. It provides an overview of the transformative potential highlighted in the study. Research, Practical & Social Implications: This part discusses the broader implications of the research. It highlights how the findings impact academia, industry, and society at large. It mentions the need for collaborative efforts and how knowledge-based entrepreneurship can drive economic growth, technological advancement, and societal progress. Originality/Value: This section underscores the uniqueness and value of the research. It emphasizes the contribution of the study to the understanding of knowledge-based entrepreneurship and its role in fostering collaboration between academia and industry. It also mentions the potential for innovation and resilience in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".