TREND: International Monetary Fund. Government Finance Statistics: Revenue: Revenue (Percent of GDP) | Country: Canada | Sector: General government | Accounting Code: G111, 1990 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 056-019-010
Bibliographic record
Abstract
datasets.shared.infosheet.CitationMgr@3f2 Dataset: Provides statistics on revenue of general government and its subsectors as a percentage of (Gross Domestic Product). Presents statistics on revenue of general government and its subsectors as reported by member countries of the International Monetary Fund (IMF). Statistics are from the Government Finance Statistics (GFS) system produced by the IMF, which is designed to provide statistics that enable policymakers and analysts to study developments in the financial operations, financial position, and liquidity situation of the general government sector or the public sector in a consistent and systematic manner. The GFS analytic framework can be used to analyze the operations of a specific level of government and transactions between levels of government as well as the entire general government or public sector. The statistical unit employed in the GFS system is the institutional unit, ie, units that affect fiscal policies, and can, in their own right, own assets, incur liabilities, and engage in economic activities and transactions with other entities. Data are typically obtained directly from the accounting records of these entities. Note that in macroeconomic statistics, provision is made for three subsectors of general government: central, state, and local. Not all countries have all three levels; and other countries may have more than three levels. In addition to levels of government, the existence of social security funds and their role in fiscal policy may require that statistics for all social security funds be compiled as a separate subsector of the general government sector. https://data-imf-org.libproxy.tulane.edu/?sk=FA66D646-6438-4A65-85E5-C6C4116C4416 Category: Government and Politics, International Relations and Trade Subject: State Government, Local Government, Public Finance, Government Receipts, Revenue, Central Government, Social Insurance Systems 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.libproxy.tulane.edu:2048/
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.133 | 0.178 |
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".