3D, can Canada make peace?: a case study of Canada's role in Somalia and Afghanistan
Bibliographic record
Abstract
The discipline of peacemaking has been evolving for the past 20 years. Somalia was Canada's first attempt to engage in peacemaking and restabilising a failed state; the mission was a failure. Currently (2010), Canada is attempting to aid Afghanistan through a whole-government or 3D policy (3D refers to the integration and coordination of Canada's departments of defence, diplomacy and development). This integrated 3D approach is severing as the cornerstone for many of Afghanistan's security, rebuilding and reconstruction projects. Through a comparative case study analysis of the integration and coordination of Canada's military, political/diplomatic structures and (brief examination) developmental tactics (which comprise the fundaments of 3D) this study demonstrates some of the initial effectiveness of a whole government approach. Preliminary findings indicate that engaging in a 3D approach has led to a more responsive strategy within the Afghan mission. Instead of utilizing the concepts of one department in a top down approach, the need to engage within three perspectives (defence, diplomacy and development) has had a significant impact for "on the ground" personal and results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.047 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".