Bibliographic record
Abstract
Argentina: the Anatomy of a CrisisJiri Jonas 1 underdeveloped country.Unlike some other countries in Latin America, it did not have rich deposits of precious metals (not that having one would have automatically ensured a more prosperous economy, though!), its more moderate climate did not allow it to grow tropical products like coffee, which was an important export commodity of other countries in the region.And the geography did not cooperate either: being so distant from main markets in Europe did not provide much impetus to trade anyway.Argentina's economic fortune began to change only in the last quarter of the 19 th century.Two important technological innovations have helped Argentina: improvement in storage and conservation of agriculture products, mainly meat, and progress in transport technology.Cheaper transport and better conservation made remote markets in Europe and North America suddenly accessible to Argentinean exporters.2 Thanks to a very fertile soil of pampas, Argentina's export of agriculture products, mainly to European markets, began to grow fast.A poor and underdeveloped Spanish colony began to turn quickly into a relatively prosperous country.Success breeds success, and Argentina became an attractive destination for labor and capital from Europe.Together with Canada, Australia and United States, Argentina attracted large amount of capital particularly from the United Kingdom, major capital-exporting country of that time.3 However, large inflow of foreign capital brought a problem well known to emerging market countries one hundred years later: financial crisis.Two powerful forces combined to accelerate capital flow to Argentina and other -at that time "emerging" -countries, Australia, United States and Canada: investors' expectations about high returns, and loose liquidity conditions and low interest rates in countries exporting capital, mainly United Kingdom.Unfortunately for Argentina, large inflow of foreign capital in the 1880s has stimulated not only investment, but also consumption.While Argentina's exports rose as well, country's capacity to improve its export earnings on a sustainable basis in order to be able to service future debt repayment did not improve sufficiently.At the same time, poor governance of domestic banks resulted in such unhealthy practices as connected bank lending and bank financing of government deficits and development projects.When monetary conditions were tightened as Bank of England increased interest rates in second half of 1889, capital flow to Argentina suddenly stopped and the country had to sharply adjust its current account balance from a deficit to a surplus, by tightening macroeconomic policy.Excesses of
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".