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Record W2133433670 · doi:10.1177/1087724x0052008

An Empirical Examination of Airframe Manufacturers’ Safety Performance

2000· article· en· W2133433670 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

VenuePublic Works Management & Policy · 2000
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsAirframeEngineeringAircraft industryProduct (mathematics)Statistical analysisAviationAeronauticsRegression analysisOrdinary least squaresOperations managementBusinessStatisticsEconometricsEconomicsMathematics

Abstract

fetched live from OpenAlex

Two firms—Boeing Company and Airbus Industrie—dominate the 100-and-more-passenger-aircraft industry. The principle focus of this study is the safety posture of the two firms’ products. We first discuss the competitive nature of the industry and previous research in commercial aviation safety before presenting a statistical analysis. The article then examines the data using the least squares regression method and the logit method; it also reviews the relationship between the variables using correlation matrices. The data investigation yields statistical evidence that over the 9-year period between 1990 to 1998, there was no significant difference in safety records between the Boeing and Airbus product lines. A data analysis for the past 4 years of the same period, however, indicates that Airbus is improving its safety posture when compared to the recent safety record of Boeing products.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.064
GPT teacher head0.457
Teacher spread0.392 · 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