On Trust: A Hard Look at Canadian Senior Officer Relationships During the Italian Campaign
Bibliographic record
Abstract
Trust and leadership go hand in hand. Trust facilitates risk taking, overcomes emotional resistance, and reinforces existing organizational norms and thus is essential to successful military leadership. This monograph examines the performance of three Canadian general officers during the Italian Campaign of World War II--Hoffmeister, Vokes, and Burns--with each case building on the last. Ending with a study of Burns explores the issues that ultimately led to his demise as a field commander. The study uses Beer's model of change (D + M + P = C), with trust added as a lens--D + M + P + (T) = C--to examine their performance as leaders and commanders. Using Beer's model to examine the leadership of these three general officers will demonstrate that trust is the missing component for this model to be an accurate leadership tool when attempting to influence behavior. In addition to the standard secondary sources, an array of journals, after-action reports, and memoirs provide context. Official archive reports offer primary evidence for the ultimate evaluation. The findings are analyzed against current theory and American and Canadian leadership doctrine. This study then provides recommendations for improving how leaders are educated and trained for positions of responsibility and, perhaps, provide a revised definition of trust.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.036 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".