MétaCan
Menu
Back to cohort

Trade, Human Capital, and Technology Spillovers: an Industry‐level Analysis

2007· article· en· W2140455069 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

VenueReview of International Economics · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsCarleton University
Fundersnot available
KeywordsAbsorptive capacityProductivityEconomicsHuman capitalInternational tradeInternational economicsCapital (architecture)Total factor productivityCapital equipmentDeveloping countryEconomic geographyIndustrial organizationGeographyEconomic growth

Abstract

fetched live from OpenAlex

Abstract This paper studies whether trade promotes North–South and South–South technology spillovers at the industry level, and how the absorptive capacity of the South affects the impact of the technology spillovers. Using data from 16 manufacturing industries in 25 developing countries from 1976 to 1998, the paper shows: (i) North–South trade‐related R&D has a substantial impact on total factor productivity in the South; (ii) South–South trade‐related R&D also promotes technology spillovers but with a smaller magnitude; and (iii) human capital is very important in facilitating North–South and South–South technology spillovers: an increase in human capital could lead to over three times the size of technology spillovers from an increase in trade‐related foreign R&D.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.045
GPT teacher head0.278
Teacher spread0.232 · 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