Bibliographic record
Abstract
Tactical understanding of the term end state can be inadequate and inaccurate when used to describe operational and strategic aims and objectives. These aims are less about ends and specific momentary conditions and more about transitions, building potential and maintaining positions of positive advantage. Therefore, military leaders transitioning from tactical execution to operational and strategic planning must divest themselves of their tactical understanding of end states and adopt a more fluid and transitionally-focused view. This study conducted a structured, focused comparison of Operation Desert Storm in Iraq from 1990 to 1991, Operation Restore Hope in Somalia from 1992 to 1993, and the Canadian operations in Afghanistan from 2001 to 2014. Four research questions were asked of each case relating to national strategic aims, military end states, the adjustments made to both, and if their flexibility led to positions of positive advantage. The case studies showed that there are several interpretations of terminology to describe operational and strategic goals. Furthermore, success came less from flexibly written strategic aims or military end state conditions and more from flexible leadership and transitional planning when creating those aims and end states. The theories and empirical evidence examined supported this monograph's thesis that clear strategic aims combined with flexibly planned military end state conditions will better maintain positions of positive advantage than the use of rigid military end states that are focused on momentary success.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".