Descent into chaos : how the war against Islamic extremism is being lost in Pakistan, Afghanistan and Central Asia
Bibliographic record
Abstract
Since 9/11, the war in Afghanistan and the invasion of Iraq, the West has been fighting a 'War on Terror', through force and through the building of new societies in the region. In this clear and devastating account, with unparalleled access and intimate knowledge of the political players, Descent into Chaos chronicles our failure. Having reported from central Asia for a quarter of a century, Ahmed Rashid shows clearly why the war in Iraq is just a sideshow to the main event. Rather, it is Pakistan, Afghanistan, and the five Central Asian states that make up the crisis zone, for it is here that terrorism and Islamic extremism are growing stronger. Documenting with precision how intimately linked Pakistan is with the Taliban and other extremist movements, while remaining the US' main ally in the region, Rashid brings into focus the role of many regional issues in supporting extremism, from nuclear programmes to local rivalries, ineffectual peace-keeping to tyrannical rulers. For Rashid, at the heart of the failure in Iraq is the US' refusal to accept the need to build nations. Ambitious and urgent, analyzing events, policies and personalities across the largest landmass in the world, Descent into Chaos chronicles with chilling accuracy why Islamic extremism is now stronger than ever.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.022 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".