Bibliographic record
Abstract
The objective of this paper is relatively straightforward, suggesting “what international air passenger travel will look like in five, ten or fifteen years and why?” This requires answering two questions; what will be the principal determinants of the growth in international air travel and what impact will each of these drivers have on the growth rate? An imbedded question is does history have anything to teach us or are there new forces at work? Canvassing the current aviation trade press finds two schools of thought, one taking the position that this a deep recession but a recession nonetheless and once world economies start recovering air traffic will go back to the typical growth of 4-5 percent annually. A second school is less sanguine, taking the position that it will not be business as usual when economies stop sinking and move to recovery. Any economic recovery is going to involve fundamental changes in institutions, rethinking polices regarding government participation in economies and changes in economic leadership in the world. There is also the hydra of protectionism most prominent now in the US but certainly being practiced elsewhere, and what will happen to foreign ownership restrictions that prior to 2009 were being seen as hurting rather than helping world airlines. All of this will change international aviation going forward.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.022 |
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".