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Record W2159119239 · doi:10.1017/s1740022807002331

The two prime movers of globalization: history and impact of diesel engines and gas turbines

2007· article· en· W2159119239 on OpenAlexaff
Vaclav Smil

Bibliographic record

VenueJournal of Global History · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Political and Social Dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPrime moverMultinational corporationGlobalizationPrime (order theory)EconomyDiesel fuelEngineeringBusinessEconomicsMarket economyFinanceAutomotive engineering

Abstract

fetched live from OpenAlex

Abstract Modern economic globalization would be impossible without our ability to move billions of tonnes of raw materials and finished goods among the continents and to fly at speeds approaching the speed of sound. These realities were made possible by the interaction of economic and technical factors. Much has been written about their organizational and political underpinnings (ranging from the role of multinational corporations to the history of free trade agreements), but much less on the history of the two prime movers that made these realities possible. Neither steam engines, nor gasoline-fuelled engines could have accomplished comparable feats. Diesel engines made ocean shipping the cheapest mode of long-distance transport and without gas turbines there would be no fast, inexpensive, mass-scale intercontinental travel. This paper examines the history, advances, benefits and costs of the two prime movers.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.010
Scholarly communication0.0040.004
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.294
Teacher spread0.283 · 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 designNot applicable
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

Citations30
Published2007
Admission routes1
Has abstractyes

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