RECURSOS NATURALES EN LA ECONOM íA: ¿ES POSIBLE EL CRECIMIENTO VERDE?
Bibliographic record
Abstract
Desde hace algun tiempo una parte de la Teoria Economica ha intentado demostrar que lapresencia de recursos naturales en la economia tiene efectos negativos en el desarrollo, entre los principales autores que han defendido esta postura han sido Jeffrey Sachs y Andrew Warner (1995), quienes marcan a las exportaciones como homonimo del avance economico, al igual que Thorvaldur Gylfason y Gilfi Zoega (2002), quienes lo miden a traves de las tasas de crecimiento. Sin embargo, las demostraciones estadisticas, en ambos documentos, parecen dejar mas preguntas que respuestas, debido al manejo de la informacion de corte transversal de todas las economias sin considerar las pertinencias geograficas, tecnologicas e historicas de cada una. En el presente articulo, se busca rebatir dichos argumentos a traves de las mismas fuentes de informacion estadistica, actualizadas, desarrollando un modelo de variables instrumentales de dos etapas entre los paises de America Latina y el Caribe con respecto a los de Europa, Estados Unidos, Japon y Canada. Se busca mostrar que la participacion de los recursos naturales en la economia, ayudan a las tasas de crecimiento, aunque al compararlo con otras variables economicas y financieras no hay evidencia clara a lo que afirman los autores mencionados.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".