Development and Evaluation of an Intuitive Operations Planning Process
Bibliographic record
Abstract
This work represents the fourth phase of a project investigating the Canadian Forces (CF) Operational Planning Process (OPP) and an alternative planning process based on intuitive decision making. This is in support of a larger project, Project Minerva, focused on reexamining Command and Control (C2), specifically the CF OPP, in the Land Force in light of the implementation of digitized C2 systems. The CF OPP represents an analytic decision making process in which 1) multiple solutions to the problem must be evaluated and the best selected, and 2) evaluation of solution alternatives must be performed through exhaustive factor-by-factor comparison. Research in the cognitive sciences has suggested that a large portion of human decision making is conducted intuitively; i.e. by less formal, non-analytic processes. Thus, there may be a mismatch between the OPP as laid out in doctrine and taught at training and education institutions within the CF, and the planning process as practiced by command teams in more operational settings, especially at the Brigade level and below. Specifically, the current work includes the development of an alternative planning process based on intuitive decision making (referred to as the Intuitive Operations Planning Process or IOPP), the development of a training course for the IOPP, and an evaluation of the effectiveness of the IOPP compared to the existing CF OPP. The IOPP exhibits the best characteristics of other intuitive planning models (Kievenaar, 1997; Schmitt & Klein, 1999; Thunholm, 2005; Whitehurst, 2002) and incorporates findings from previous work investigating application of the OPP in the CF (Bruyn et al., 2005), while maintaining a large amount of the terminology, outputs generated and formal staff briefings used in the OPP in order to promote level of acceptance by CF practitioners and face validity of the IOPP.
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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 | 0.027 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".