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Record W6901639106 · doi:10.6068/dp15eba4c841553

TREND: International Monetary Fund. Balance of Payments: Capital Account | Country: Canada | International Monetary Fund Subject: Capital account | Code: 209BA, 1948 - 2015. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 056-006-007

2017· other· en· W6901639106 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCapital accountCapital (architecture)Current accountBalance of paymentsNet capital ruleCapital formationFinancial capitalCurrencyFixed capital

Abstract

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International Monetary Fund. Balance of Payments: Capital Account | Country: Canada | International Monetary Fund Subject: Capital account | Code: 209BA, 1948 - 2015. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 056-006-007 Dataset: The capital account shows (1) capital transfers receivable and payable between residents and nonresidents and (2) the acquisition and disposal of nonproduced, nonfinancial assets between residents and nonresidents, as reported by International Monetary Fund member countries. The balance on the capital account presents the total credits less debits for capital transfers and nonproduced, nonfinancial assets. The sum of the current and capital account balances can also be shown as a balancing item, labeled as net lending (+)/net borrowing (–) from the capital and current accounts. That sum is conceptually equal to net lending (+)/net borrowing (–) from the financial account, although conceptually equal, they may differ in practice: the current and capital accounts show nonfinancial transactions, with the balance requiring net lending or net borrowing, while the financial account shows how net lending or borrowing is allocated or financed. For each country, jurisdiction, or other reporting entity, the time series are provided as a balance and as credit vs debit in national currency as available, and in United States dollars. The Balance of Payments Statistics (BOPS) database, published by the International Monetary Fund (IMF), contains time series of quarterly and annual BOPS data for more than 180 countries, jurisdictions, or other reporting entities. For some of these countries, the data have been supplemented by data IMF economists have derived from other sources. BOPS summarizes the economic transactions of a country with the rest of the world. It reports total goods, services, factor income, and current transfers an economy receives from or provides to the rest of the world, as well as capital transfers and changes in each economy’s external financial claims and liabilities. http://data.imf.org/?sk=7A51304B-6426-40C0-83DD-CA473CA1FD52 Category: Banking, Finance, and Insurance, International Relations and Trade Subject: International Economic Organizations, Balance of Payments, Capital 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/

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.391
Threshold uncertainty score0.787

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.021
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0040.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1780.239

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.

Opus teacher head0.023
GPT teacher head0.281
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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