Comparison of U.S. Security Strategies in Iraq and Afghanistan (1991–2021): Objectives, Methods, and Outcomes
Bibliographic record
Abstract
This study provides a comparative analysis of the United States’ security strategies in Iraq and Afghanistan from 1991 to 2021. It focuses on examining the objectives, methods, and outcomes of U.S. interventions in these two countries. The U.S. intervention in Iraq was primarily aimed at regime change, establishing democracy, and countering Iranian influence. In contrast, the main focus in Afghanistan was on combating terrorism and preventing the country from becoming a safe haven for terrorist groups. The U.S. security strategies in Iraq and Afghanistan differ significantly. In Iraq, a combination of hard and soft power was employed, whereas in Afghanistan, hard power played a more dominant role. The findings of this research indicate that despite initial military successes, the U.S. failed to achieve its long-term strategic goals in either country. Political and security instability, the emergence of extremist groups such as ISIS, and the increased influence of regional players like Iran and Russia were among the consequences of these strategies. This study highlights that the U.S. security approaches largely failed due to their neglect of social, cultural, and political factors. Ultimately, the research suggests that future policymakers should adopt a balanced mix of hard and soft power in security interventions and place greater emphasis on the local conditions of the target country.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".