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Record W6976473311 · doi:10.6068/dp16e3c880daa41

TREND: World Trade Organization. WTO Annual Trade in Merchandise and Services: Merchandise - Imports | Country: Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burma, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo (Brazzaville), Congo (Kinshasa), Cook Islands, Costa Rica, Cote D'Ivoire, Croatia, Cuba, Cyprus, Czech Republic, Denmark, Djibouti, Dominica, Dominican Republic, East Germany, East Timor, Ecuador, Egypt | Indicator: Textiles, 1980 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 082-001-003

2019· other· en· W6976473311 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCommodityEuropean unionBalance of paymentsWorld tradeBalance of tradeProduct (mathematics)PaymentStatistical analysis

Abstract

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World Trade Organization. WTO Annual Trade in Merchandise and Services: Merchandise - Imports | Country: Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burma, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo (Brazzaville), Congo (Kinshasa), Cook Islands, Costa Rica, Cote D'Ivoire, Croatia, Cuba, Cyprus, Czech Republic, Denmark, Djibouti, Dominica, Dominican Republic, East Germany, East Timor, Ecuador, Egypt | Indicator: Textiles, 1980 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 082-001-003 Dataset: Reports merchandise imports by region, selected regional agreements, and economy. Statistics on merchandise trade cover total merchandise retained exports by region, selected regional agreements, and economy. Breakdowns by major commodity groups for individual economies, selected regional trade agreements, and regions at the total level are also included. Commodity groups are based on the SITC product nomenclature. Reports exports and imports of commercial services and merchandise trade. Statistics on trade in commercial services are sourced from the IMF Balance of Payments Statistics and from the Trade in Services by Partner Country dataset of the OECD. Data for European Union members, EU candidate and EU observer countries as well as the EU(28) aggregate are drawn from Eurostat. For some economies, data are drawn from national sources. Where possible, reported data are complemented by estimations produced by the WTO, UNCTAD and ITC. Merchandise trade statistics are mainly sourced from national sources and complemented with estimations produced by the WTO. Data for individual European Union members are drawn from Eurostat. Additional data sources include UNSD COMTRADE, the IMF International Financial Statistics, and UNCTAD. For more information on the statistical sources, compilation methodologies and definitions of groups please refer to the Technical Documentation). https://www-wto-org.proxy.library.nyu.edu/english/res_e/statis_e/trade_datasets_e.htm Category: International Relations and Trade Subject: International Trade, Imports, Merchandise Source: World Trade Organization Established in 1995, the World Trade Organization (WTO) provides a forum for negotiating agreements aimed at reducing obstacles to international trade and ensuring a level playing field for all. The WTO also provides a legal and institutional framework for the implementation and monitoring of these agreements, as well as for settling disputes arising from their interpretation and application. The current body of trade agreements comprising the WTO consists of 16 different multilateral agreements (to which all WTO members are parties) and two different plurilateral agreements (to which only some WTO members are parties). As of 2018, the WTO has 164 members, of which 117 are developing countries or separate customs territories. WTO activities are supported by a Secretariat of some 700 staff, led by the WTO Director-General, located in Geneva, Switzerland. https://www-wto-org.proxy.library.nyu.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 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)0.007
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 score0.942
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.016
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0700.092

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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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
Published2019
Admission routes1
Has abstractyes

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