Comparison of the Cf Doctrinal and Applied Operational Planning Process
Bibliographic record
Abstract
Several alternative models of operational planning have been proposed that adopt an intuitive or recognition-primed decision strategy, compared to one that is analytical. Although these models have been generated from anecdotal reports and observations of planning in operational settings, a systematic comparison of doctrinal and applied planning processes has never been documented. This paper describes the work done to empirically compare the planning process, as applied during a Brigade (Bde) level exercise, to the doctrinal Canadian Forces (CF) Operations Planning Process (OPP). Observers followed and documented all of the planning functions performed by the Plans Cell using the CFOPP as a reference. Results suggest that: the applied OPP, conducted at Brigade level, is a hybrid and abbreviated version of doctrinal OPP; planning is somewhat opportunistic and involves intuitive decision making by both the Commander and Staff; the OPP, as applied in an operational setting, is a command-driven process, and, intuitive decision making at individual (lower) function level by Staff member could lead to more efficient planning. These results have implications for doctrine, training, OPP process refinement and planning tool design.
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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 | 0.029 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".