Bibliographic record
Abstract
La innovacion debe considerarse como una cultura en los paises, debe fomentarse y promocionar esta practica, ya que los beneficios que pueden surgir a partir de esta pueden ser determinantes para las empresas y por consiguiente para la economia de los paises. Implementar la gestion de proyectos de innovacion en conjunto de los sectores privado, publico e instituciones universitarias o de investigacion, pueden generar beneficios importantes y generar mayor valor agregado a las empresas (Arancibia, Donoso, Venegas y Cardenas, 2015).El objetivo de este documento es determinar hacia donde van dirigido los proyectos de innovacion en algunos paises de Sur, Centro y Norte America, conocer cuales son los factores determinantes para que estos proyectos se generen y cual es la situacion actual en materia de innovacion. Se realizo revision de articulos economicos en la base de datos SCOPUS, sobre la innovacion en los paises de Colombia, Chile, Mexico, Brasil, Costa Rica y Canada, para descubrir la situacion actual de estos procesos y cual es su tendencia durante los ultimo veinte anos.
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".