Application of Hierarchical Goal Analysis to the Halifax Class Frigate Operations Room: A Case Study
Bibliographic record
Abstract
This paper reports on the first application of Hierarchical Goal Analysis (HGA) [1], a relatively new approach to requirements analysis for complex systems, to naval command and control. HGA, applied to 11 positions of the Canadian Forces Halifax Class Frigate operations room, decomposed three top-level goals to a full goal hierarchy of 563 goals. The hierarchy ranged from four to nine levels deep, with an operator assigned to each goal. The HGA process concluded with a stability analysis for identifying potential goal conflicts and an upward flow analysis for identifying requirements for feedback between operators. An examination of the stability analysis revealed that the current design of the operations room includes few sources of instability where multiple operators compete for control of the same variable. The upward flow analysis revealed that the requirement for feedback from operators assigned to lower-level goals to operators assigned to higher-level goals is relatively high, and the operations room could benefit from review and redesign. The goal hierarchy, operator assignments, stability and upward flow analyses, and proposed solutions were reviewed by subject matter experts. While used to model an existing system, the present application of HGA appears to be especially useful in providing a basis for evaluating a system design and developing design recommendations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".