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Record W3048206913 · doi:10.4236/ojbm.2020.85118

An Economic Study of the US Post-9/11 Aviation Security

2020· article· en· W3048206913 on OpenAlexaboutno aff
James Michael Ford, Ardeshir Faghri, Dian Yuan, Saumabha Gayen

Bibliographic record

VenueOpen Journal of Business and Management · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsAviationAirport securityRevenueTerrorismBusinessEconomic securityFinanceEconomicsEconomic growthComputer securityEngineeringPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The Aviation Security world changed drastically following the terrorist attacks of September 11th, 2001. In this paper we look at 1) the changes that occurred to the aviation security sector and 2) how the United States aviation security compares to other parts of the world. Currently the United States has the most expensive aviation security infrastructure in the world. The main motivation of this topic was to find out why the United States was spending so much and assessing whether its aviation security sector was economically efficient. In this paper the authors provide the history of aviation security and the changes that took place post 9/11. A cost breakdown is presented and whether the amount of money being spent is worth the benefits received is discussed. This study also compares the United States’ aviation security to that of Europe and Canada. These comparisons analyze how the total expenditure for the U.S. Transportation Security Administration (TSA) is similar/dissimilar to the aviation security expenditures in Europe and Canada. Recommendations for future budgets and tax revenues are also made. Overall, it is concluded that the amount of TSA’s spending on aviation security is justified.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.267

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 integrity0.0000.000
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 head0.036
GPT teacher head0.246
Teacher spread0.209 · 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 teacher head, 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

Citations4
Published2020
Admission routes1
Has abstractyes

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