The AVAC-COM Communication Model and Taxonomy: Results from Application to Aviation Safety Events
Why this work is in the frame
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Bibliographic record
Abstract
Communication problems are acknowledged as hazardous eventualities affecting operations negatively. However, a few systematic attempts have been made to understand the pattern of communication issues and their contribution to safety events. In this paper, we present the AVAC-COM communication model and taxonomy based on the cybernetics approach and a literature review. The model elements and taxonomy variables regard the actors, signals, coders, interference, direction and timing, predictability, decoders, and channels. To test the applicability and potential value of the AVAC-COM framework, we analysed 103 safety investigation reports from aviation published between 1997 and 2016 by the respective authorities of Canada, the United States, Australia, the United Kingdom and the Netherlands. The overall results of the 256 cases of communication flaws detected in the reports suggested that these regarded more frequently Human-Media and Human-Human interactions, verbal and local communications as well as unfamiliarity of the receivers with the messages transmitted. Further statistical tests revealed associations of the region, time period, event severity and operations type with various variables of the AVAC-COM taxonomy. Although the findings are only indicative, they showed the potential of the AVAC-COM model and taxonomy to be used to identify strong and weak communication elements and relationships in documented data such as investigation and hazard reports.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it