Global aviation human resource management: contemporary compensation and benefits practices
Bibliographic record
Abstract
The commercial airline is an extremely competitive, safety‐sensitive, high technology service industry. People, employees and customers, not products and machines, must be the arena of an organisation’s core competence. The implications are vast and pervasive affecting no less than the organisation’s structure, strategy, culture, and numerous operational activities. Completed by 13 respondents (executives), this audit presents a series of select findings of a human resource management audit carried out in 2001‐2 and contains extensive data on airlines from nine countries from around the globe. The conclusion drawn from these three bodies of work is that, with the exception of a handful of high performing airlines, the industry as awhole continues to function as per a traditional, top‐down, highly divisionalised, industrial model of operations and governance. This model is manifestly inappropriate in such a highly knowledge‐based service market as the airline industry. HRM expertise in general and compensation and benefits in particular are required now,more than ever, to spearhead the strategic development of a customer‐centric, learning‐oriented workforce that is capable of adapting quickly to the strategic goals and change imperatives facing the airline industry.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".