The Constant Changes in US Strategy in Afghanistan: Achievement or Failure
Bibliographic record
Abstract
The nations take various strategies in exposure to different developments and phenomena and impact on foreign and internal policies of countries in international scene proportional to their internal and external conditions and rivals and at international arena. What US implemented after September 11 Event and targeted accusation finger toward Taliban and Al-Qaeda in Afghanistan is deemed as a type of strategy that has occurred in created nostalgic climate together with hasty decision making and negligence to domestic issues in Afghan Community while their output was to take different and even paradoxical strategies in this crisis-stricken region since 1980s. In this article that has been written in order to analyze US Post September- 11 Strategies in Afghanistan this basic question will be answered that how changes in US macro policies influenced in orientation of diplomacy of this country and why this country has adapted different policies in occupation of Afghanistan. Afterwards, it is deduced according to the given findings from librarian data collection method that the constant changes in US strategy in Afghanistan were due to overlooking of domestic issues and historic, ethnic, cultural, political, and ideological complexities of this country that has resulted in degradation of US position in world scene and its failure in suppression of Taliban.This article has been excerpted from my PhD treatise under title of ‘The role of United States in the regional crisis (e.g. Afghan and Iraqi crises) and the rise of revolutionary and radicalism on the emergence of international terrorism’.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".