Development and Evaluation of an Intuitive Operational Planning Process
Bibliographic record
Abstract
Although formal planning procedures are key parts of military doctrine, they may not be well suited to highly dynamic, time-pressured environments. The authors describe an intuitive planning process developed as an alternative to the procedure used by the Canadian Forces (CF). The Intuitive Operational Planning Process (IOPP) treats planning as a highly iterative process of incremental refinement in which a single course of action is elaborated and continually evaluated for its suitability. To examine the effectiveness of the IOPP, 12 members of a reserve CF Civil Military Cooperation unit of Land Force Central Area acted as planning staffs and created plans for two simulated planning exercises. Participants employed the IOPP for one scenario and the existing CF Operational Planning Process (OPP) for another. The teams were able to successfully employ the IOPP to develop acceptable plans; however, it was not possible to determine statistically that the quality of these plans surpassed that of plans generated with the OPP because of limitations of the data set. The IOPP was judged to be very easy to use, but teams expressed less trust in it than the existing OPP. The IOPP may foster greater collaboration and commander involvement in planning than the OPP.
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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 | 0.025 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".