Organizaciones de apoyo: conectando redes para la innovación empresarial en clústeres de un país en desarrollo
Bibliographic record
Abstract
Este artículo examina el papel de las organizaciones de apoyo (OA) en la promoción de la innovación y competitividad en empresas paraguayas. Se analizan seis clústeres importantes en el país: carnes, lácteos, cerámica y construcción, textil, farmacéuticos y químicos. Se reconstruyen las redes de colaboración utilizando datos de entrevistas a empresas y OA. Se emplean análisis de redes, regresiones econométricas y análisis cualitativo de las entrevistas. Los resultados revelan cómo las empresas acceden a recursos externos para mejorar su competitividad e innovar a través de las OA del clúster. En el entorno de un país en desarrollo, resulta fundamental la cooperación entre estas organizaciones en redes que trascienden sectores y territorios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".