The Real Twin Towers: Al Qaeda's Influence on Saudi Arabia and Pakistan
Bibliographic record
Abstract
ARIF LALANI I think it is fitting that we are ending with a focus on something concrete, namely Pakistan and Saudi Arabia. We have spent most of the day talking about issues across the globe: transnational issues, theories, and trends. But at the end of the day, what matters, in fact, is what is happening on the ground. And the topic before us, al Qaeda's influence on Pakistan and Saudi Arabia, could be reworded to ask, “How successful has the campaign against al Qaeda been?” To answer the question of what al Qaeda's influence is at the moment in places like Pakistan and Saudi Arabia is really to answer that question about everything we've been discussing today. HAMID MIR Preliminarily, I want to share something with you. Some days ago, a Pakistani newspaper published a story about this conference. That story was filed by Mr. Khalid Hassan, who is a very senior and well-respected journalist based in Washington. I did not read the story because on that day I was in the tribal area where the Pakistan army is fighting against al Qaeda. In the evening when I came back, I received a call on my cell phone and somebody was saying, “You are going to Washington and there is a big controversy. You are going to speak against us there and if you go, you must be ready to face the consequences.”
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".