Relaciones intersectoriales en latinoamérica en el período 1980-99: Un análisis econométrico
Bibliographic record
Abstract
espanolEl objetivo del estudio es analizar las diferencias de desarrollo economico existentes entre los paises latinoamericanos, teniendo en cuenta su situacion en 1980 y la evolucion durante el periodo 1980-99. En el analisis econometrico se combinan datos de 22 paises y se mide el impacto de la evolucion de la agricultura y de la industria sobre el sector servicios, destacando las causas y consecuencias del debil impulso que la industria ha tenido en muchos paises durante dicho periodo. En el analisis de las diferencias de crecimiento industrial se tienen en cuenta las interrelaciones existentes entre la produccion, el comercio exterior, la educacion y otros factores sociales e institucionales, y se destacan las politicas que pueden evitar los desequilibrios economicos e impulsar el crecimiento. EnglishThis paper analyses the differences in economic development among the Latin America countries, taking into account its situation in 1980 and its evolution along 1980-99. In the econometric analysis we include data for 22 American countries, including also US and Canada, in order to show the impact that the evolution in agriculture and industry has had over the services sector. In this connection, it has to be mentioned the sparse increase of industry in this period in many of the Latin American countries. In order to explain the differentials in industrial growth, we consider the inter-relationships among output, foreign trade, education and other institutional and social factors. Finally, we recommend some policies addressed to avoid economic imbalances and to foster economic growth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".