Evaluación del impacto de corto plazo de SENACYT en la innovación de las empresas panameñas
Bibliographic record
Abstract
El presente documento presenta una evaluación preliminar del impacto que el programa de subvenciones a la investigación y desarrollo tiene en la dinámica innovadora de las firmas panameñas. Dentro de las limitaciones de los escasos datos disponibles, la metodología resuelve el problema de la atribución mediante el empleo de métodos cuasi-experimentales. El ejercicio hace uso de información nueva contenida en la reciente encuesta de innovación de Panamá, conjuntamente con información de registro de las firmas beneficiadas. Lamentablemente, dado el corto horizonte temporal disponible para la evaluación, solamente resultados de corto plazo han podido ser monitoreados. Dentro de estas limitaciones, se encuentra que las empresas beneficiarias han sistemáticamente incrementado su esfuerzo innovador y que ello se ha visto reflejado en un aumento en la incidencia de invención tecnológica a un nivel precompetitivo.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".