Bibliographic record
Abstract
Abstract Entrepreneurial archetypes are configurations of organizational attributes – typically of strategy, organization design, environment, and governance – that represent very common types of entrepreneurship in their context. The utility of these types is that they distinguish among different types of entrepreneurship, the situations in which these occur, the challenges they typically encounter, and the behaviors required for optimal firm performance under those conditions. The significance of archetypes is that, a rather few types can often describe in a fine‐grained way a large fraction of organizations or complex situations. Such empirically or conceptually derived types segment the complex world of organizations into more homogeneous and analytically tractable compartments. They allow researchers to make key distinctions among different types and situations, and thus more powerful generalizations and predictions within them. Indeed, a few variables often can be used to categorize a firm into a type, and from that categorization predictions can be made regarding the states of other variables, their relationships, and financial performance. This focus on archetypes gave rise to the early work on entrepreneurial orientation. Common entrepreneurial archetypes include business creation by a founding entrepreneur, innovation in an expanding enterprise, and expansion by a corporate builder.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".