Interagency Trust in the Whole of Government Approach to Canadian Forces Operations: Subject Matter Expert Discussions
Bibliographic record
Abstract
Abstract : In current operations (e.g., Afghanistan and Haiti), the Canadian Forces (CF) are expected to work closely with a number of other government departments (OGDs) in order to achieve a full range of national objectives. The Whole of Government (WoG) approach aims to consolidate the Government of Canada's (GoC) strategic policy regarding international engagements. However, in practice, some WoG partners have limited experience working together and have different organizational cultures, which may hinder effective collaboration in practice. The purpose of this study was 1) to further understand the impact of interagency trust (or interorganizational trust) on collaboration efforts between civil and military actors in a WoG approach to operations, and 2) to generate recommendations for CF education and training regarding interagency collaboration. To this end, a number of subject matter experts (SMEs) were consulted to elicit their first-hand accounts of collaboration efforts in theatre, specifically highlighting those instances that signalled trust. Participant recollections revealed a number of organizational factors (e.g., strategy, systems, structure, and staffing) that may impede interorganizational trust in the WoG context. Civilian participants mentioned that they had to establish their credibility working in theatre by consistently delivering high quality input on a timely basis. Interpersonal trust was developed over time. Participants recommended that WoG education and training need to be fully integrated with the participation of both civilian and military agencies.
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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 | 0.049 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.044 | 0.012 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".