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Record W3123336309 · doi:10.3386/w9017

Intermediaries in the U.S. Market for Technology, 1870-1920

2002· report· en· W3123336309 on OpenAlexaff
Naomi R. Lamoreaux, Kenneth L. Sokoloff

Bibliographic record

VenueNational Bureau of Economic Research · 2002
Typereport
Languageen
FieldArts and Humanities
TopicHistory of Science and Natural History
Canadian institutionsUniversity of Toronto
FundersUniversity of California, Los AngelesNational Science Foundation
KeywordsIntermediaryBusinessCommerceMarketing

Abstract

fetched live from OpenAlex

We argue that the emergence of a well-developed market for patented technologies over the late nineteenth and early twentieth centuries facilitated the emergence of a group of highly specialized and productive inventors by making it possible for them to transfer to others responsibility for developing and commercializing their inventions.The most basic of the institutional supports that made this market possible was, of course, the patent system, which created secure and tradable property rights in invention.But trade was also facilitated by the emergence of intermediaries who economized on the information costs associated with assessing the value of inventions and helped to match sellers and buyers of patent rights.Patent agents and lawyers were particularly well placed to provide these kinds of services, because they were linked to similar attorneys in other parts of the country and because, in the course of their regular business activities, they accumulated information about participants on both sides of the market for technology.Our quantitative analysis of assignment contracts demonstrates that patentees whose assignments were handled by these specialists produced more patents over their careers, assigned a greater fraction of their patents, and also were able to find buyers for their inventions much more quickly than other patentees.In other words, the development of institutions supporting market trade in patented technology seems to have made it possible for creative individuals to specialize more fully in inventive work --that is, it seems to have set in motion the kind of Smithian processes that have generally been associated with higher rates of productivity growth.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0030.004
Scholarly communication0.0060.004
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.391
GPT teacher head0.464
Teacher spread0.073 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2002
Admission routes1
Has abstractyes

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