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Record W6958104878 · doi:10.6068/dp161257b9eca33

TREND: Organisation for Economic Co-operation and Development (OECD). OECD Factbook 2014: Economic, Environmental and Social Statistics: Globalization - Foreign Direct Investment | Country: China | Socioeconomic Indicator: Outward FDI Stocks, 2010 - 2012. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 062-001-028

2018· other· en· W6958104878 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2018
Typeother
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentRestrictivenessGlobalizationChinaEquity (law)Stock (firearms)Socioeconomic statusForeign portfolio investment

Abstract

fetched live from OpenAlex

Organisation for Economic Co-operation and Development (OECD). OECD Factbook 2014: Economic, Environmental and Social Statistics: Globalization - Foreign Direct Investment | Country: China | Socioeconomic Indicator: Outward FDI Stocks, 2010 - 2012. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 062-001-028 Dataset: Foreign direct investment (FDI) is defined as investment by a resident entity in one economy that reflects the objective of obtaining a lasting interest in an enterprise resident in another economy. The lasting interest implies the existence of a long-term relationship between the direct investor and the enterprise and a significant degree of influence by the direct investor on the management of the enterprise. The ownership of at least 10% of the voting power, representing the influence by the investor, is the basic criterion used. FDI measures reported here include outward and inward investment stocks in millions US dollars, where inward stocks refer to all direct investments held by nonresidents in the reporting economy; and outward stocks are the investments of the reporting economy held abroad. Corresponding flows, also expressed in millions US dollars, relate to investment during a period of time. Negative flows generally indicate disinvestments or the impact of substantial reimbursements of inter-company loans. The FDI index, where Closed=1 and Open=), gauges the restrictiveness of a country's FDI rules through four types of restrictions: foreign equity limitations; screening or approval mechanisms; restriction on key foreign employment; operational restrictions. The OECD FDI restrictiveness indexes presented here demonstrate that more open economies receive more FDI. This dataset provides indicators included in the OECD Factbook 2014: Economic, Environmental, and Social Statistics, updated annually by the Organisation for Economic Co-operation and Development (OECD). Indicators, reported in 12 broad subject areas, cover a wide range of topics: agriculture, economic production, education, energy, environment, foreign aid, health, industry, information and communications, international trade, labor force, population, taxation, public expenditure, and research and development. Data are provided for all OECD member countries and Brazil, China, India, Indonesia, Russia, and South Africa, where available. NOTE: The data presented here are copyrighted by OECD and reproduction is subject to OECD permissions policies: See http://www.oecd.org/rights for further information. Indicator descriptions are based on the OECD Factbook 2014. http://stats.oecd.org/BrandedView.aspx?oecd_bv_id=factbook-data-en&doi=data-00590-en Category: International Relations and Trade Subject: International Trade, International Investment, Financial Indexes, Foreign Investment Source: Organisation for Economic Co-operation and Development (OECD) Established in 1961, when 18 European countries plus the United States and Canada joined together to create an organization dedicated to global development, the Organisation for Economic Co-operation and Development (OECD) today includes 34 member countries from around the globe, ranging from North and South America to Europe and the Asia-Pacific region. Member countries include many of the world’s advanced countries as well as emerging nations. The OECD mission remains the promotion of policies that will improve the economic and social well-being of people around the world. The OECD collects and analyzes data on a broad range of topics to help governments foster prosperity and fight poverty through economic growth and financial stability, at the same time taking the environmental implications of economic and social development into account. The OECD Secretariat collects and analyzes data, after which committees discuss policy regarding this information, the Council makes decisions, and then governments implement recommendations. The performance of individual countries is monitored following implementation via a system of multilateral surveillance and a peer review process. The OECD is headquartered in Paris, France, and it is funded by its member countries. National contributions are based on a formula that takes account of the size of each member's economy. The largest contributor is the United States, which provides nearly 24% of the budget, followed by Japan. http://www.oecd.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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0830.023

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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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