Exportación de madera aserrada de conífera chilena. Un análisis de su competitividad
Bibliographic record
Abstract
Se analiza la competitividad en los principales mercados de las exportaciones chilenas de madera aserrada de pino radiata en el período 1994-2004 (Contreras, 2006). Se aplica la metodología “Competitive Análisis of Nation” (CAN), creada por la Comisión Económica para América Latina y el Caribe (CEPAL), que permite obtener una visión dinámica de las situaciones de competitividad del producto en los mercados estudiados. Los resultados se compararon con un estudio similar del periodo 1987 a 1997, realizado por Fuenzalida (1999). El análisis de la competitividad en el periodo 1994- 2004 muestra que el producto tiene una posición competitiva de Estrella Naciente, en los mercados de China, España, Estados Unidos. y Marruecos; de Estrella Menguante en los mercados de México y Canadá; de Oportunidad Perdida en Japón, Arabia Saudita y Reino Unido; y de Retroceso, en Corea del Sur. Por otro lado, comparando los dos periodos en estudio, se encontró que los mercados de Estados Unidos, Corea del Sur, Japón, Reino Unido y Arabia Saudita, que se repiten en ambos periodos, disminuyeron su posición competitiva del primer periodo al segundo. Asimismo, se puede concluir que el comercio mundial de productos forestales creció más que el comercio de madera aserrada. AbstractThe competitiveness is analyzed on the principal markets of the Chilean exports of saw wood of radiata pine in the period 1994-2004 (Contreras, 2006). The methodology applied is “Competitive Analysis of Nation” (CAN), created by the Economic Commission for Latin America and the Caribbean (CEPAL), who allows to obtain a dynamic vision of the situations of competitiveness of the product on the studied markets. The results were compared with a similar study of the period 1987 to 1997 realized by Fuenzalida (1999). The analysis of the competitiveness in the period proves to be 1994-2004 that the product has Nascent Start’s competitive position, on the markets of China, Spain, United States and Morocco; of Waning Start’s position on the markets of Mexico and Canada; of Lost Opportunity in Japan, Saudi Arabia and United Kingdom; and of Setback in South Korea. On the other hand, comparing both periods, one thought that the markets of United States, South Korea, Japan, United Kingdom and Saudi Arabia, which they repeat in both periods, diminished their competitive position of the first period to the second one. Likewise, it is possible to conclude that the world trade of forest products grew more than the trade than saw wood.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".