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Record W1523835624 · doi:10.1017/s0022050717000018

Technology and Geography in the Second Industrial Revolution: New Evidence from the Margins of Trade

2017· article· en· W1523835624 on OpenAlexaff
Michael Huberman, Christopher M. Meissner, Kim Oosterlinck

Bibliographic record

VenueThe Journal of Economic History · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBoomInternational tradeDestinationsProductivityInternational economicsEconomicsBusinessEconomic geographyGeographyTourismEngineering

Abstract

fetched live from OpenAlex

Belle Époque Belgium recorded an unprecedented trade boom. Exploiting a new granular trade dataset, we find that the number of products delivered abroad and destinations serviced more than doubled in less than 40 years. To explain this remarkable achievement, we study the relationship between trade costs and the intensive and extensive margins of trade. The establishment of a foreign diplomatic network that lowered beachhead costs and enabled the entry of new products was an essential fact of the trade boom. Interestingly, the expansion in trade in certain sectors did not translate into faster productivity growth. We offer some explanations.

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)0.001
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.124
GPT teacher head0.224
Teacher spread0.099 · 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

Citations40
Published2017
Admission routes1
Has abstractyes

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