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Record W6958006893 · doi:10.6068/dp14badfea9954

Trend 05/1994 - 02/2014. World Bank. Global Economic Monitor: Imports Merchandise, Customs, Constant USD, Millions, Seasonally Adjusted | Country: Canada, 05/1994-02/2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 051-003-021.

2015· other· en· W6958006893 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)National accountsEconomic dataNational bankEconomic forecastingForeign-exchange reservesOrder (exchange)Economic indicatorAgency (philosophy)World Development Indicators

Abstract

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World Bank (2015). Global Economic Monitor: Imports Merchandise, Customs, Constant USD, Millions, Seasonally Adjusted | Country: Canada, 05/1994-02/2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 051-003-021. Dataset: Reports imports of merchandise (goods), cost, insurance and freight basis (c.i.f.), in constant millions of United States dollars, seasonally adjusted. Constant series show the data for each year in the value of the base year 2005. The data are reported by country,for low-income and middle-income countries by geographic region (ie, Economic Reporting Entity), and by country income level. The Global Economic Monitor (GEM) dataset includes economic indicators on 196 countries. Data are provided where available by country, country group, and/or reporting entity. Statistics presented include interest rates, exchange rates, equity markets, bond spreads, consumer prices, Gross Domestic Product, industrial production, and merchandise trade.The World Bank obtains this data from its own databases, the International Monetary Fund and other sources. The Bank normalizes the data to the extent possible to support country-country comparisons. Data sources and years of coverage vary across indicators. Much of the data comes from the statistical systems of World Bank member countries. The quality of the data depends on how well these national systems perform. Errata announcements are posted periodically by the World Bank at http://data.worldbank.org/about/data-updates-errata. Category: International Relations and Trade Source: World Bank Founded in 1944 as a result of the Bretton Woods Conference, the World Bank is an independent specialized agency of the United Nations that provides financial and technical assistance to developing countries in order to foster economic growth and reduce poverty. Today, the World Bank Group comprises five institutions owned by 187 member countries: the International Bank for Reconstruction and Development (IBRD), which focuses on middle-income and creditworthy poor countries; the International Development Association (IDA), which focuses on least developed countries; the International Finance Corporation (IFC); the Multilateral Guarantee Agency (MIGA); and the International Centre for the Settlement of Investment Disputes (ICSID). http://www.worldbank.org/ Subject: Imports, Merchandise, International Trade

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)0.001
metaresearch head score (Gemma)0.010
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 score0.350
Threshold uncertainty score0.704

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.021
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0940.087

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 head0.031
GPT teacher head0.274
Teacher spread0.243 · 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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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