An Analysis of the Main Features of the Business Incubators
Bibliographic record
Abstract
Over time the success of business incubators has been questioned. There were observers who have noticed their beneficial effects (Haugen 1990), but there were also observers who have criticized the incubators and their impact, for example Cote (1991) who was highly critical with the incubators from Canada, funded by the government. Regarding the business incubators we should remember that the objectives of the incubators should be fully taken into account in evaluating their success. If the objective is to create new companies with a higher probability of success than a non-start-up incubator, then the criteria should be the long-term survival rates and the leaving moment from the incubator. On the other hand, if the objective of an incubator is to obtain profit as an independent entity, then the number of successful companies after leaving the incubator wouldn’t be an appropriate criterion for determining the success of the incubator. Throughout this work we aim to capture the main features of business incubators and to turn our attention to the results of incubation Projects led in Romania through The National and Multiannual Program of Establishment and Development of Technological and Business Incubators (2002-2012).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".