Collaboration between the Canadian Forces and the Public in Operations
Bibliographic record
Abstract
Abstract : In current operations, the Canadian Forces (CF) are expected to work more closely than in the past with a number of diverse civilian organizations, including Non-Governmental Organizations (NGOs), International Organizations (IOs), Other Governmental Departments (OGDs), local populations, and the media. However, the CF's history of working with NGOs, for example, has been limited and may pose challenges to collaboration. The purpose of this study was twofold: (1) to further understand the core issues that help or hinder civil-military collaboration involving the CF, NGOs, IOs, Afghan nationals, and the media; and (2) to elicit from subject matter experts (SMEs) recommendations for potential training and education that may assist in making collaboration in theatre more effective for the diverse, multiple parties involved. SMEs representing diverse organizations and entities, both military (CF) and civilian (NGOs, IOs, Afghan nationals, the media), were consulted to elicit first-hand accounts of collaboration efforts in the Afghanistan theatre of operations. Data were collected from Sep 27, 2010 to Jan 7, 2011 using a semi-structured protocol that guided discussions on five core themes: negotiation, power, identity, stereotypes/prejudice, and trust. Results indicate that the CF did not effectively acknowledge their counterparts' expertise and experience, and that they should refrain from taking charge and telling others how to do their jobs. Civilian participants said that the CF engaged in open dialogue, and that CF leaders were good at engaging, but that they could engage more with civilians and civil organizations given the challenges faced by civilians in navigating the military system. Military and civilian participants said that one strategy to facilitate collaboration was to build positive relationships. Civilian SMEs thought that the military sometimes overstepped its jurisdiction and that roles and responsibilities needed to be clearly established.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".