Benchmarking de modelos de incubación mexicanos versus el modelo de Innovacorp
Bibliographic record
Abstract
Resumen Si bien se ha investigado sobre la incubación de negocios, poco se comenta sobre las mejores prácticas en este ámbito para lograr que las incubadoras en México puedan sobresalir internacionalmente. Por ello, la presente investigación aborda las practicas que ha desarrollado “Innovacorp”, una incubadora situada en una de las provincias más pequeñas de Canadá, y cómo ha logrado ser reconocida a nivel internacional, para comparar su desempeño con tres modelos de incubación utilizados en México, resaltando sus similitudes y diferencias en términos de vinculación, creación y atracción de clientes, sus criterios de admisión y graduación de empresas, infraestructura, inversión, tutoría, medición de indicadores y seguimiento a empresas. Al hacer la comparación se observan diferencias tanto en las prácticas de incubación como en el enfoque y necesidades de los negocios que se atienden en ambos países. Abstract While there has been research on business incubators, little has been said about their best practices in order to help Mexican incubators to excel internationally. Therefore, this paper addresses the incubation practices developed by “Innovacorp” a business incubator located in one of the smallest Canadian provinces and how it has managed to be recognized internationally, in order to compare its performance with three different incubation models used in Mexico, highlighting their similarities and differences in terms of business linkage, creation and attraction of customers, admission and graduation criteria, infrastructure, investment, mentoring, measurement indicators and business monitoring. When comparing them, differences are observed both in incubation practices as well as in the business needs addressed in both countries.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".