Numerous, Capable, and Well-Funded Rebels: Insurgent Military Effectiveness and Deadly Attacks in Afghanistan
Bibliographic record
Abstract
Why do some of Afghanistan's provinces experience more deadly attacks on counterinsurgents than others? We argue that provinces with more militarily effective insurgents will be deadlier for the forces of the counterinsurgency. We posit that insurgent military effectiveness is an interactive function of the rebel group's size, the quality of its recruits, and the group's operational budget. More militarily effective insurgents should, in turn, produce more deadly violence against Coalition forces. We model this relationship at the provincial level in Afghanistan using negative binomial regressions. Ultimately, we find that in provinces where the insurgency is more militarily effective, deadly attacks against counterinsurgent forces occur more often. Based on this finding, we conclude with directions for future research and policy recommendations for both the current operations in Afghanistan and for future counterinsurgency campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".