Competitiveness of the Chilean Economy in the Period of Its Liberalization
Bibliographic record
Abstract
Liberalization of the Chilean economy from a quarter of a century ago was intended to promote modernization of the economy and mobilization of new stimulators of the economic growth. The economy was to be financed with proceeds from the exports and inflow of foreign capital. The above strategy could succeed if the economic reforms had led to an increase in the competitiveness of the Chilean economy. In the paper, the liberalization process, the transformation of the Chilean exports and its sources, the impact of exchange rate changes on the exports' competitiveness, as well as changes in the Chilean export sector following the Asian crisis, were presented. The analysis confirmed that, in fact, the development of the export sector during the last quarter of a century largely contributed to the Chile's economic success. However, the considerable opening to the world market, with the exports structure showing a great dependence on low processingdegree goods, makes the country's economy extremely vulnerable to external shocks. If the export sector is to remain the most important source of the growth trends, a further increase in its competitiveness will be indispensable, and that will be difficult to achieve without a considerable diversification of the exports. Also, prevention of an excessive currency appreciation is of great importance for the maintenance of an exportdriven growth model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".