Bibliographic record
Abstract
The International Air Transport Association (IATA) has often been thought of as a representative organization that is exclusively focused on airlines. This article, however, shows that IATA is a many-faceted organization that meets the needs of various key airline industry sectors, including airports. Course training is one of the areas IATA uses to meet airport needs. Under the management of the IATA Training and Development Institute (ITDI), based in Montreal, training is conducted through several Regional Training Centers, including Geneva, Miami, Singapore, Beijing, and Delhi (the newest RTC set to open in 2008). Training is in the form of both classroom courses and distance learning courses, involving self-study. Airport-specific training comprises 10% of the course offerings. Examples of the most popular airport courses during the 2004-2007 period include: Safety management, Operations, Strategic management, Certification and Standards, Planning, and Commercial management. Other course offerings are specific to the needs of airlines, civil aviation, cargo handlers, and human resources operations. ITDI also offers three Airport Training Diplomas; these include Advanced Airport Safety Management Systems, Airport Strategic Management, and Advanced Airport Operations. Course offerings in 2008 will emphasize training for senior level industry management via an executive education program. The curriculum for this program will address customer service, branding, and strategic planning, some of the areas ITDI sees as vital for airport managers who need to stay ahead of the next developments in the industry. A total of 157 airport-specific courses have been conducted worldwide by ITDI over the last four years, involving 320 airline industry companies.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.026 |
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".