The air transport research society world conference: A data science-based literature review on the years 2014–2024
Bibliographic record
Abstract
The Air Transport Research Society (ATRS) World Conference is one of the major venues for air transport research. The conference covers a wide range of research talks, practice/industrial sessions, and research workshop activities. In this paper, we perform a data-driven analysis of the research abstracts that have been accepted and presented at the conference since 2014. We have grouped the abstracts from the ten annual conferences using t-distributed stochastic neighbor embedding to map high-dimensional keyword vectors into a two-dimensional plane for clustering, analysis, and visualization. The major focus of our study concerns three directions. First, we provide a formal description of the actual research presented at the ATRS World Conference series by using methods from natural language processing and machine learning, leading to a data-driven classification consisting of 35 major subject categories. Second, we analyze the origin of main authors/presenters and their background, including their institutions and countries of origin. Third, we perform a network-driven analysis of co-authorships across abstracts to identify the role and importance of key researchers in the community. Finally, we provide an analysis of popular research topics indicated by authors when submitting their abstracts, and a set of major recommendations for future work, based on the insights obtained from our study.
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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.058 | 0.053 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".