Contextualizing Lean Startup and Alternative Approaches for New Venture Creation: Introducing the Special Issue
Bibliographic record
Abstract
The Lean Startup movement fundamentally changed entrepreneurial education and the way new ventures evolve. While Steve Blank and other founders of the movement embraced academic ideas, the movement grew among practitioners largely disconnected from academic entrepreneurship research. The purposes of this special issue are (1) to better connect Lean Startup practice to academic entrepreneurship research and (2) to advance theory regarding Lean Startup practices and their outcomes. After a brief and personal story of Lean Startup’s beginnings by its founder, Steve Blank, the first set of papers in this special issue juxtapose Lean Startup with alternative approaches to new venture creation developed by scholars outside the influence of the Lean Startup movement. The second set of papers describe how Lean Startup might be contextualized for different unique situations. The third set dives into different Lean Startup practices to help researchers and practitioners think more deeply about decisions and trade-offs made during implementation of Lean Startup.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".