New Operations Decision Support Requirements derived from a Control Theory Model of Effects-Based Thinking
Bibliographic record
Abstract
New operations such as effects-based approach to operations, comprehensive approach to operations, or net-enabled operations (the Canadian name for Net Centric Operations) promise to facilitate effective interaction between people, technology, and effects while engaging a borderless but networked adversary. These new operations require effective decision support for critical decisions in lieu of potentially large amounts of communications and information sharing. The operations ’ decision support requirements are derived by looking at Effects-Based Thinking (EBT) from a Control Theory perspective, and then identify the operations’ fundamental characteristics in a simple and straightforward manner. The EBT Control Theory perspective has a sequence of effects-based activities – planning, execution, assessment, decision-making, and analysis – expressed as a feedback control system structure. After studying each activity’s process, organization, and technology, key decision support requirements are derived as follows: 1) Decision matrices should provide decision options based on desired and current effects. 2) Human-computer interfaces should display the status of effects. 3) Decisions should complement each other and be made known. 4) Staffs should understand their competencies, authorities, responsibilities, and the mission intent. 5) Computer technologies should support communications and information sharing. Arguably, these requirements will provide an engagement advantage when implemented in new operations.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".