‘You don’t need to love us’: Civil-Military Relations in Afghanistan, 2002–13
Bibliographic record
Abstract
In recent years the growing involvement of militaries in the delivery of assistance in conflict-affected areas under the rubric of stabilisation or comprehensive approaches has become a key concern for humanitarian agencies, raising questions about the adequacy of existing guidance and current approaches to civil-military coordination. In order to better understand the challenges of principled and effective dialogue between military forces and independent humanitarian actors in the context of combined international and national military forces pursuing stabilisation, this article charts the evolution of the civil-military dialogue in Afghanistan from 2002 until 2012. Drawing on semi-structured interviews with a range of former staff of aid agency, military, and donor organisations who were present in Afghanistan in this period as well as audits, official guidelines, and other written documents, this article provides an analytical overview of the development of stabilisation approaches in Afghanistan and the strategies aid agencies pursued in response, in particular the trajectory of mechanisms for structured dialogue. Lastly, it identifies several implications that can be drawn from this experience for aid agencies, NATO, and troop contributing nations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".