Fighting the Mujahideen: Lessons from the Soviet Counter-Insurgency Experience if Afghanistan
Bibliographic record
Abstract
The Canadian Forces currently in Afghanistan as part of a NATO coalition are playing a major role in a counter-insurgency campaign directed against a resurgent Taliban threat. Dealing with this menace will not be easy as the Taliban, realizing they cannot defeat NATO’s superior military strength, have resorted to asymmetric actions that strike at the coalition’s will through the cumulative effects of terror and small-scale “hit and run” military operations. Although NATO must contend with these tactics, concentrating solely on the military aspects of the problem will not address the real danger.\nThe true nature of the Taliban’s threat rests in its political strength and not in its military capability. In order to destroy the Taliban’s influence in the region a combination of political, social, economic, and military means are necessary. The complexities of dealing with these issues in a coherent manner are significant, but in the case of Afghanistan there is no precedent. Interestingly, the Soviets faced many of these same challenges while fighting a counter-insurgency campaign against the Mujahideen through much of the 1980s.1\nThe Soviet experience should be of interest to coalition members as it provides a contemporary example of the challenges of conducting counter-insurgency operations within that country. Contrary to popular belief, the Soviets followed a logical and multifaceted, if somewhat brutal, counter-insurgency strategy in Afghanistan. A critical examination of the Soviet performance reveals that many of their failings can be directly attributed to a lack of resources and in this respect, there are a surprising number of similarities between the operational environment the Soviets faced and the situation that now confronts NATO. This paper will explore aspects of Soviet counterinsurgency operations during their occupation of Afghanistan, and assess strengths and weaknesses relevant to current operations in that country.2
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".