Change-Oriented Risk Management in Civil Aviation Operation: A Case Study in China Air Navigation Service Provider
Bibliographic record
Abstract
Change-oriented risk management is the key content of civil aviation safety management. Hazard identification is considered as one of the most difficult and flexible parts. To address the risk management due to changes introduced in existing systems, in this paper, a system change-oriented hazard identification (SCOHI) model is firstly proposed. The SCOHI model identifies hazards by integrating “5M” (mission-man-machine-management-medium), and hazard and operability (HAZOP) techniques specify changes in a system and the associated impacts on the surrounding environment. Compared with the traditional brainstorm process, the SCOHI model provides an explicit way for hazard identification in a dynamic environment. Then, taking an air navigation service provider (ANSP) in Northwest China as an example, a case study of system changes from nonradar control operations to radar control operations is analyzed. The effectiveness and applicability of the SCOHI model are tested with a risk assessment. The results from the preliminary evaluation show that the four key system change-oriented hazards are air traffic control (ATC) skills, staff capacity, control procedures, and airspace structure. In addition, the “Man” category accounts for around 55% of the total risk, ranking number 1, followed by “Management,” “Medium,” and “Machine” categories. Finally, a sound risk control strategy is provided to the ANSP to help in controlling the risk and maintaining an acceptable level of safety during system changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".