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Record W4368351168 · doi:10.1016/j.jmoneco.2023.05.002

Trade and diffusion of embodied technology: an empirical analysis

2023· article· en· W4368351168 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Monetary Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversity of British ColumbiaBank of CanadaUniversity of Toronto
Fundersnot available
KeywordsEmbodied cognitionProduction (economics)EconomicsConstruct (python library)DiffusionKnowledge flowMeasure (data warehouse)EconometricsInstrumental variableIndustrial organizationInternational tradeBusinessMicroeconomicsComputer scienceKnowledge managementData mining

Abstract

fetched live from OpenAlex

Using global patents, citations, inter-sectoral sales, and trade data, we examine the international diffusion of technology through imported inputs. We use citations and sales data to characterize knowledge and production input-output tables for individual countries. Using these tables, we construct a measure of the flow of knowledge-weighted and production-weighted technology embodied in inputs imported from the US. We develop an instrumental variable strategy to establish that increases in embodied technology imports lead to increased innovation and knowledge diffusion in sectors within importing countries. Effects are substantially larger for knowledge-weighted imports of embodied technology.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.240
Teacher spread0.202 · 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