Direct Economic Impact Analysis of the World’s Top Five Busiest Airports in 2018
Bibliographic record
Abstract
The purpose of this study is to analyze the direct economic impacts of the world’s top five busiest airports in 2018 as they contribute to the economic well-being of the larger communities they serve. This study uses a descriptive case-study methodology since the direct economic impacts of the world’s top five busiest airports are going to be studied in a case-by-case with an intention of reporting the research findings that are not related to specific variables. Amongst the 17,678 commercial service airports in the world, Hartsfield-Jackson Atlanta International Airport (USA), Beijing Capital International Airport (Peoples Republic of China), Dubai International Airport (United Arab Emirates), Los Angeles International Airport (USA), and Tokyo Haneda International Airport (Japan) have been ranked respectively as the top five busiest airports in the world on the basis of passenger volume handled in 2018. The research findings indicate that these airports have tremendously benefited their respective communities in terms of employment generation, income generation, and total direct economic impacts. These airports have generated a total of $181.4 billion worth of direct economic benefits to their respective communities and regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".