Bibliographic record
Abstract
Vast expanses of desert, rainforest, mountain, and coastal biomes sprawl across Peru, Bolivia and Chile.These diverse countries hold not only striking beauty throughout these regions, but also vast natural resources.In the Atacama Desert region, the discovery of such important natural resources have left residual problems lingering in the economic and political relations between these South American nations, and still cause tensions today.This paper explores historic disputes over resources on the borders where all these nations meet, and how they affect current politics and economic relationships, specifically related to Bolivia's desire to regain access to the sea.Then, current solutions and development of the problem are analyzed, and a suggested long-term solution is presented that could best address the border disputes.Territorial controversies in the Atacama Desert region where Peru, Chile and Bolivia meet are not a recent development.Rather, many current disputes arise from arguably unresolved border issues from past wars treaties.In the 1940s, it was discovered that "valuable quantities of nitrate of soda could be extracted from guano and nitrates (saltpeter), both of which were abundant in Peru", for use as a fertilizer and for making gunpowder (Foster, 190).These dual purposes forced demand for nitrates in developed countries, especially those in Europe, to greatly increase.Through this new market, the Atacama Desert area in southern Peru and parts of Bolivia and Chile transformed from an uninhabited desert to a desirable, profitable region.Chileans attempted to take advantage of these profits by mining in the disputed region between Chile and Bolivia, so Bolivia "attempted to raise taxes on exports by Chilean intermediaries of nitrates", breaking a past treaty (Foster, 190).This led Chile to declare war on both Bolivia and Peru, who were secret allies at the time.Through the victories of their superior navy and infantry, Chile was able to "seize Bolivia's Atacama province and Peru's Tarapacá", including "all of the nitrate zones in Bolivia and Peru and most of Peru's coastal guano deposits" (Foster, 191).This loss of resources and land caused Peru to lose one of their main exports and fall into a recession, as well as causing Bolivia to become landlocked.The tensions that arose from the war still exist between the three nations, causing social, political and economic strains on relations and current events in the region.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".