“Just one of many donors”: Canada’s engagement with civil society in Afghanistan
Bibliographic record
Abstract
Canada’s entry into Afghanistan as a NATO ally was widely considered a necessary military venture, but Canadian operations during the reconstruction period that ensued were criticized as having tarnished Canada’s reputation on the international stage. Canada’s history as a peacekeeping nation and its perceived middle power status did initially allow it to act as a mediator between the Afghan government, local actors, and the international community. However, our research discovered that Canada did not fully understand the dynamics of the established civil society structures in Afghanistan, nor the ways in which they influence local politics. A limited understanding of “civil society” among Canadian officials and disproportionate focus on professionalized NGOs as its most legitimate representative meant that local civil society groups in Afghanistan and the role they could play to establish democratic structures were largely ignored. By complementing knowledge synthesis of existing scholarly and policy literature with interviews with Canadian and Afghan aid workers, this article examines the process of post-conflict rebuilding in Afghanistan, considering how neglecting Afghan civil society has resulted in less durable social structures. We also examine Canada’s image as an international mediator, and identify new ways of assisting rebuilding processes to ensure more durable and peaceful outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.063 | 0.014 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".