Saudi Arabia and the global Islamic terrorist network : America and the West's fatal embrace
Bibliographic record
Abstract
Cutting Free From the Saudi Oil Noose R.J.Woolsey The Scandal of United States-Saudi Relations D.Pipes Who Is Behind the Muslim Mainstream Organizations? S.Emerson Shariah-Compliant Finance: Saudi Arabia's Trojan Horse F.Gaffney The Stealth Saudi Jihad into the American Mind S.Stern The Stealth Curriculum S.Stotsky The Saudi Penetration into American NGOS D.K.Shideler & I.Weinglass The Saudis on J Street L.Ben-David All Politics is Local: Co-workers of the Truth Fight Jihad in Fairfax J.Lafferty Unprecedented Challenges Confronting our Constitution by Radical Islam D.Yerushalmi Their Oil is Thicker Than Our Blood R.Ehrenfeld The Impact of the Organisation of Islamic Cooperation on Europe B.Ye'or The Organisation of Islamic Cooperation, Defamation of Religions, and Islamophobia D.Weiss The Green Corridor, Myth or Reality?: Implications of Islamic Geopolitical Designs in the Balkans S.Trifkovic Canada: Islamism's Happy Hunting Ground D.Harris The Way Forward: Looking Backward and Looking Inward S.Stern & K.Shideler
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".