Three Models of Foreign Policy Learning in Counterinsurgency: David Galula and the Algerian War
Bibliographic record
Abstract
Counterinsurgency (COIN) has recently received much attention in IR. However, emphasis has been on why insurgents often win. This essay asks instead how individuals and states learn to conduct COIN campaigns. IR lacks a consensus theory of foreign policy learning. I offer a pluralist account, arguing multiple processes of learning are possible. I emphasize three. The first is top down, predicating learning on rigid systems of theoretical assumptions. This is learning by paradigm. The second is bottom up -- learning begins with trial and error experiments, gradually clarifies policy problems, and then forms solutions. This is learning by doing. The third locates learning in intersubjective recognition, framed by cognitive scientists as a process of mentally replicating or simulating the mental states of others. This is learning by simulation. I evaluate this heterogeneous account against a historical case of COIN learning: the French COIN theorist David Galula’s experiences in the Algerian War. Galula’s account was and is especially influential, creating much of what we now consider standard COIN theory. I conclude that all three models offer insight into foreign policy learning. Thus, a pluralist account of foreign policy learning is appropriate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".