Reconstructing Afghanistan: Civil-military experiences in comparative perspective
Bibliographic record
Abstract
"This book identifies some of the main lessons for civil-military interactions that can be derived from the experiences of Provincial Reconstruction Teams (PRTs) in Afghanistan. The book has three main themes. Firstly, the volume analyses why the ways in which civil and military actors interact in theatres of operations such as Afghanistan matter ... for both those categories of actors, and for the ordinary people who their interactions serve. Second, the book highlights that these interactions are invariably complex. The third theme, which arises specifically from 'the PRT experience' in Afghanistan, is that such teams vary significantly in their roles, resourcing, and operational environments. Consequently, to appraise the value of 'the PRT experience', it is necessary to unpack the experiences of different PRTs, which the use of case studies allows one to do. The volume comprises an introduction, identifying some key questions to which the PRT experience gives rise, and case studies of the experiences of the United States, United Kingdom, New Zealand, Canada, The Netherlands, Australia, Germany and France; chapters dealing with the roles played by NGOs and the UN system and a discussion from an Afghan perspective of the implications of civilian casualties. It is the combination of the diverse cases discussed in this book with a focus on the broad challenges of optimising civil-military interactions that makes this book distinctive"..
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.038 | 0.027 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".