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Record W3036182197 · doi:10.5430/rwe.v11n3p245

Labour Productivity in Different Segments of Aircraft Industry

2020· article· en· W3036182197 on OpenAlexvenueno aff
Л Б Соболев

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Mathematical Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityIndustrial organizationPurchasing power parityCompetition (biology)Liberian dollarBusinessPurchasing powerWorkforceEconomicsBargaining powerLabour economicsFinanceExchange rateMicroeconomicsEconomic growthMacroeconomics

Abstract

fetched live from OpenAlex

This article is devoted to the problem of labor productivity (LP) in various segments of the global aircraft industry over the past decade. Intense competition forces aircraft manufacturers to pursue a policy of saving resources (primarily, workforce) at the all stages of the life cycle of aircraft and to introduce innovative technologies, automation, and robots. In assessing the relative growth of LP, the inflation (relative to the base 2009) and the purchasing power parity (PPP) of national currencies relative to the dollar are taken into account. The analysis showed that the LP is different for aircraft market segments and depends on development of market relations in the countries-manufacturers. The author believes that the main difficulty to the LP growth in countries with developing markets are monopolism, weak management, and insufficient skills of engineers, marketers, and workers.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.112
GPT teacher head0.332
Teacher spread0.220 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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