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Record W2767021851 · doi:10.60082/2817-5069.3196

Economic Approaches to Intellectual Property, by Nicola Searle and Martin Brassell

2017· article· en· W2767021851 on OpenAlexaffvenue
Alexandria Chun

Bibliographic record

VenueOsgoode Hall law journal · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsYork University
Fundersnot available
KeywordsIntellectual propertyValue (mathematics)BusinessLegislationIntangible assetLaw and economicsCoca colaAsset (computer security)Intangible propertyEmpireCommerceAdvertisingLawEconomicsAccountingComputer securityPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Intellectual property (IP) is well recognized as an important economic asset. Many companies rely heavily on their IP to contribute value to their enterprise: Apple has built an empire on its trademarked logo and branding, Coca-Cola on its secret soft drink formula, and Pfizer on its pharmaceutical patents (to name just a few). The economic value of IP and its strong relationship to business is even reflected in IP legislation. For example, trademark statutes in various jurisdictions share an underlying policy of promoting free and fair competition in trade.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, 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: none
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.236
GPT teacher head0.230
Teacher spread0.006 · 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
Published2017
Admission routes2
Has abstractyes

Explore more

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