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Record W3036182197 · doi:10.5430/rwe.v11n3p245

Labour Productivity in Different Segments of Aircraft Industry

2020· article· en· W3036182197 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.112
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