Bibliographic record
Abstract
In this book, author Nader Moumneh–a Canadian senior policy adviser of Lebanese descent– examines the research of the formation and evolution of the Christian resistance in Lebanon he performed as a graduate student at the American University of Beirut in the early 1990s. He has conducted hundreds of lengthy interviews with senior Lebanese Forces leaders who were thoroughly impressed by his communicative yet assertive personality, his scrupulous presentation of facts, his obsessive attention to detail, and most importantly, his unwavering determination to unveil behind-the-scenes events. Mr. Moumneh drew upon his self-acquired persuasion tactics and negotiation strategies to earn the Lebanese Forces’ trust and gain access to top secret, never-before published information. Since then, he has continually revised and expanded the manuscript to address the rapidly changing situation in Lebanon and the Middle East. The Lebanese Forces: Emergence and Transformation of the Christian Resistance has taken twenty-five years to produce and is unique in its own right. Mr. Moumneh’s work is not a typical re-telling of the Lebanese crisis, rather it is a magnificent blend of skillful craftsmanship, an unprecedented wealth of painstakingly referenced chronological research and now declassified intelligence information.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".