TREND: International Monetary Fund. International Financial Statistics: International Reserves | Country: Argentina, Australia, Belgium, Brazil, Canada, Chile, China, Costa Rica, Czech Republic, Denmark, Finland, France, Germany, Hong Kong, Indonesia, Ireland, Italy, Japan, Malaysia, Mexico, Netherlands, Peru, Philippines, Russia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, United Kingdom, United States | ID: RAFA_USD | Indicator: Official Reserve Assets, 1950 - 2016. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 056-015-008
Bibliographic record
Abstract
International Monetary Fund. International Financial Statistics: International Reserves | Country: Argentina, Australia, Belgium, Brazil, Canada, Chile, China, Costa Rica, Czech Republic, Denmark, Finland, France, Germany, Hong Kong, Indonesia, Ireland, Italy, Japan, Malaysia, Mexico, Netherlands, Peru, Philippines, Russia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, United Kingdom, United States | ID: RAFA_USD | Indicator: Official Reserve Assets, 1950 - 2016. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 056-015-008 Dataset: Provides statistics on total reserves minus gold, gold holdings, other foreign assets and foreign liabilities of the monetary authorities, and foreign accounts of other financial institutions. Where significant, foreign accounts of financial corporations other than the central bank are also reported. The unit of account for the IMF IFS is the SDR, or Special Drawing Rights. The SDR is an international reserve asset, created by the IMF in 1969 to supplement its member countries’ official reserves. Its value is currently based on a basket of five major currencies, as of October 1, 2016. SDRs can be exchanged for freely usable currencies. The database covers approximately 32,000 time series covering more than 194 countries and areas starting in 1948. Topics covered include balance of payments, commodity prices, exchange rates, fund position, government finance, industrial production, interest rates, international investment position, international liquidity, international transactions, labor statistics, money and banking, national accounts, population, prices, and real effective exchange rates. Data availability varies by country and time period. http://data.imf.org.proxy-um.researchport.umd.edu/?sk=5dabaff2-c5ad-4d27-a175-1253419c02d1&sid=1390030109571&ss=1452193181026 Category: Banking, Finance, and Insurance, International Relations and Trade Subject: Gold, Assets, Finance, Liabilities Source: International Monetary Fund Headquartered in Washington, DC, the International Monetary Fund (IMF) was conceived at a United Nations conference convened in Bretton Woods, New Hampshire, United States, in July 1944. The 44 governments represented at that conference sought to build a framework for economic cooperation that would avoid a repetition of the vicious circle of competitive devaluations that had contributed to the Great Depression of the 1930s. As of 2015, the IMF has 188 member countries. Its primary purpose is to ensure the stability of the international monetary system, specifically the system of exchange rates and international payments that enables countries (and their citizens) to transact with one other. This system is essential for promoting sustainable economic growth, increasing living standards, and reducing poverty. The Fund’s mandate has recently been clarified and updated to cover the full range of macroeconomic and financial sector issues that bear on global stability. The IMF is a specialized independent agency of the United Nations but has its own charter, governing structure, and finances. Its members are represented through a quota system broadly based on their relative size in the global economy. http://www.imf.org.proxy-um.researchport.umd.edu/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.144 |
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".