Succeeding at Organizational Transformation: a Study of Literature, Western Military Organizational Transformations in the Last Seventy Years, and Comments on the Current Canadian Forces Transformation
Bibliographic record
Abstract
Organizational transformations are inherently difficult to achieve in any public or private sector. As such, there has been a significant body of writing developed primarily in business and the military as to how to achieve successful organizational transformations. The intent of this paper is to synthesize these writings down to five key principles that will then be used to examine several recent, western, military transformations. They include the attempt at U.S. unification in the 1940s, the implementation of the Goldwater-Nichols Act of 1986, the creation of the Australian Defence Force in the 1970s and Canadian Forces unification in the 1960s. Once these historical examples have been used to test the five principles of a successful organizational transformation, the current attempt at a transformation of the Canadian Forces will be examined to determine if that endeavor is likely to succeed or not.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.024 |
| Science and technology studies | 0.025 | 0.048 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".