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Record W2471494886 · doi:10.1111/1758-5899.12353

Sovereign Patent Funds: Sovereign Wealth Funds 2.0?

2016· article· en· W2471494886 on OpenAlexaff
Warren Clarke

Bibliographic record

VenueGlobal Policy · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSovereign wealth fundGlobal assets under managementSovereigntyScope (computer science)BusinessInvestment (military)Sovereign statePoliticsEconomicsFinanceForeign direct investmentInstitutional investorPolitical scienceLawMacroeconomicsCorporate governance

Abstract

fetched live from OpenAlex

Abstract Significant academic and policy attention has focused on identifying the causes and consequences of the growth of traditional sovereign wealth funds ( SWF s). This paper, in contrast, highlights a new type of sovereign investment vehicle – sovereign patents funds ( SPF s) – that have emerged primarily in advanced industrialized economies, including France, South Korea and Japan. As defined here, SPF s are investment funds that seek to acquire intellectual property resources deemed strategically valuable in the pursuit of national economic objectives. This brief survey article considers the implications of SPF s in comparison to more traditional sovereign wealth funds. In doing so, it asks what emerging discourses on sovereign patent funds can learn from the sovereign wealth fund debate, and points to both similarities and important differences between these entities. While highlighting similarities between SPF s and traditional SWF s, the paper notes that patent funds are more explicitly political than their SWF counterparts. Following an overview of both SPF s and SWF s, the paper compares elements of the structure, behaviour, and response to these funds. Finally, while a full assessment of the activities and merits of SPF s lies beyond the scope of this article, the conclusion offers key lessons drawn from the SWF comparison.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.029
GPT teacher head0.244
Teacher spread0.215 · 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.

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

Citations10
Published2016
Admission routes1
Has abstractyes

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