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Record W3159964190

지식재산의 이전과 주식 -From intellectual property to equity shares-

2006· article· ko· W3159964190 on OpenAlexaboutno aff
이성상, 이정동, 류태규

Bibliographic record

Venue지식재산연구 · 2006
Typearticle
Languageko
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)Intellectual propertyBusinessPrivate equity fundExploitEquity capital marketsPrivate equityFinanceEconomicsPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

Many research institutions transfer their intellectual property to new firms founded to exploit it and tend to receive equity in a new firm instead of, or in combination with, cash royalty like annual royalties on sale. Especially in U.S., U.K. and Canada where business infrastructure is well organized for new firms, research institutions have positive attitude to equity licensing. Even in other regions where entrepreneurial infrastructure is weak, taking equity has also been considered as an important method of IP transfer. Because taking equity is likely to expedite IP transfer and maximize expected payoff to the research institutions. And the application of IP transfer with equity can be explained as a response to the trend in which research institutions are becoming more entrepreneurial. This paper examines types and application processes of IP transfer with equity. And we review the need for the application of IP transfer with equity and raise several points on applying of equity licensing in the regulations in force. The result of this study can help research institutions to set up their effective strategy in IP transfer.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.020

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 head0.033
GPT teacher head0.249
Teacher spread0.217 · 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
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
Published2006
Admission routes1
Has abstractyes

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