MétaCan
Menu
Back to cohort
Record W2911595051 · doi:10.4337/9781788116633.00022

US patent sales by universities and research institutes

2020· book-chapter· en· W2911595051 on OpenAlexaboutno aff
Brian J. Love, Erik Oliver, Michael J. Costa

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2020
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsnot available
Fundersnot available
KeywordsCommercializationQuarter (Canadian coin)BusinessAccountingEliteMarketingIntellectual propertyPolitical science

Abstract

fetched live from OpenAlex

This Chapter explores the extent to which universities and other nonprofit research institutes currently participate in the secondary market for patents. We document 220 assignments, involving a total of 544 US patent assets, that appear to represent arms-length patent sales by universities (or other nonprofit research institutes) during the period 2012–2017. We present data on the entities and assets involved in these transactions, as well as the publicly available circumstances underlying each sale. Among other findings, we observe that foreign universities are the most active market participants. Overall, US universities and labs account for less than one quarter of sales, and elite US research universities are almost entirely absent from the market. We also find that few academic US patent sales bear the hallmarks of technology transfer. Just eleven percent of assets appear to have been purchased with commercialization in mind. Virtually all other purchases appear to have been either defensive acquisitions by operating technology companies or purchases by nonpracticing entities. Finally, we consider what conclusions policymakers and university administrators may draw from our data.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.683
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.132
GPT teacher head0.254
Teacher spread0.122 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEdward Elgar Publishing eBooksSame topicInnovation Policy and R&DFrench-language works237,207