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Pension Funds ’ Investment Position in the Second Decade of XXI Century

2019· article· en· W2980300204 on OpenAlexaboutno aff
E. V. Emelianov

Bibliographic record

VenueInternational Trade and Trade Policy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsPensionGlobal assets under managementPosition (finance)National PensionInvestment (military)Fund of fundsBusinessInstitutional investorInvestment strategyFinanceInvestment policyPension fundAssets under managementEconomicsFinancial systemForeign direct investmentFixed assetProduction (economics)Corporate governanceMacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

The article explores investment position of pension funds which become important actors in the national economies and world investment flows; with comparative analysis of the pension funds based in different countries with different models of pension systems and investment regulatory practices. The role of pension funds as investors is based on accumulating growing funding which become nearly half of total OECD gross domestic product. The assets of pension funds in the second decade of the century are concentrated in US, United Kingdom, Canada, with pension funds in other countries less than 5% for each country. But assets of pension funds based in some other countries show significant growth. The article focuses on the pension funds’ assets structure and compares those in different countries. The perspectives of investment pension assets in the national economies and abroad will depend among other factors on the regulation of pension funds and their investments. Focusing on ensuring better access to different investment opportunities in the domestic market and abroad should go hand in hand with raising standards of risk management in pension fund investment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.303
Teacher spread0.278 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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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