Examining public-facing statements on airport websites related to aerial drones
Bibliographic record
Abstract
This study examines the public-facing statements that can be found on airport websites related to aerial drones. Data were extracted via manual web scraping from 288 different airports’ official websites across 69 different countries. To be selected, airports had to be one of the 100 busiest airports in terms of passenger numbers in 2017, and (or) be one of the IATA slot coordinated and facilitated airports as of 10 November 2020. Manual web scraping was completed by using Google site searches for the keywords “unmanned”, “drone”, and “remotely piloted”. Phrases, sentences, and paragraphs containing these keywords were collated for each airport and then thematic analysis was undertaken to identify themes within the data. Surprisingly, this study finds that 143 (49.65%) of the airports have no mention of the keywords on their websites. For those that did have statements, thematic analysis revealed 20 themes, of which the largest three were regulation, ensuring compliance, and enforcement (38.54%, 29.17%, and 25.35% of airports, respectively). There were significant differences in the number of statements overall and within specific themes based upon airport location; however, there were no statistically significant differences based upon how many passengers the airport handles.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".