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Record W2702745219 · doi:10.1111/ehr.12430

Shakeout in the early commercial airframe industry

2017· article· en· W2702745219 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

VenueThe Economic History Review · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsQueen's University
Fundersnot available
KeywordsAirframeGovernment (linguistics)Aircraft industryMarket shareBusinessMarketingEngineeringAeronautics

Abstract

fetched live from OpenAlex

Abstract The commercial airframe industry in the US experienced a shakeout from the early 1930s into the post‐Second World War period. Unlike shakeouts in automobiles, tyres, or televisions, the commercial airframe industry's early life cycle was affected by external factors, particularly government demand. Using newly digitized data on all planes introduced in the commercial market between 1926 and 1965, we find that commercial airframe manufacturers with bomber contracts during the Second World War were more likely to have postwar market share than firms without such contracts, controlling for plane characteristics and other forms of government contracting. We attribute the effect of bomber contracts to advantages in R&D learning capacity acquired by firms with military airframe contracts. Despite low (or zero) initial presence in the commercial market, these learning capacity advantages allowed such firms to survive the early period of the shakeout, and later to thrive.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.005

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 head0.127
GPT teacher head0.274
Teacher spread0.147 · 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