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Record W3035966467

High Technology Royalty Survey Financial Terms: Descriptive Statistics and Analysis

2012· article· en· W3035966467 on OpenAlexaboutno aff
Jack Lu

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
Fundersnot available
KeywordsLicenseeDescriptive statisticsPaymentLump sumBusinessMarketingStatisticsActuarial scienceMathematicsFinanceLicenseComputer science
DOInot available

Abstract

fetched live from OpenAlex

The High Technology Sector of the Licensing Executives Society (U.S.A. and Canada) conducted the first deal term and royalty survey in 2011. A total of 228 licensing transactions were reported, and 220 of them included various forms of royalty payments. This article analyzes the descriptive statistics of the lump sum payments and the flat running royalty rates expressed as percentages of sales. The average of 119 transactions with sales-based percentage rates is about 6%, and the median is 5%. Lump sum payments vary drastically across transactions, and the median is about half million. The analysis further tabulates descriptive statistics by characteristics of licensor/licensee, performance of licensed products, number and type of IP licensed, field of use, technology type and development stage, and certain licensing terms such as exclusivity, co-marketing, and auxiliary payments.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.217
Teacher spread0.177 · 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 designObservational
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
Published2012
Admission routes1
Has abstractyes

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