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Record W2755354040 · doi:10.33423/jabe.v20i6.381

Slowdown Aviation and Modernity’s Lost Dynamism

2018· article· en· W2755354040 on OpenAlexvenueno aff
Saifedean Ammous

Bibliographic record

VenueJournal of Applied Business and Economics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsnot available
Fundersnot available
KeywordsDynamismAviationPoliticsSlowdownState (computer science)ModernitySAFERPolitical sciencePolitical economyMarket economyAeronauticsEconomyEconomicsEngineeringLawComputer scienceAerospace engineeringComputer security

Abstract

fetched live from OpenAlex

The inevitability of technological progress is one of the most widely-held beliefs of our era but there is one very important field in which technological progress appears not only to have stagnated but reversed. Examining the technical and commercial reality of modern aviation, there are three objective markers of slowdown. First, airplanes operational today can no longer reach the speed and altitude records set four decades ago. Second, commercial flight times have not only failed to get shorter, they actually take longer than they did in the past. Third, forty years after its introduction, supersonic flight is no longer available for civilians, neither in commercial nor private jets. While flight today is safer, cheaper, and more widespread than in the past, it has become slower, and jet makers are strangely not even interested in exploring ways of making it faster. The paper concludes with a discussion of the cultural, political, economic, and institutional reasons behind this slowdown. The receding state of the art in aviation acts as both an object lesson and a warning for the state of economic dynamism overall.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.019
Scholarly communication0.0060.009
Open science0.0000.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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