The Meeting of civilizations: Muslim, Christian, and Jewish
Bibliographic record
Abstract
The horrific acts of anti-Western and anti-Jewish terrorism carried out by Muslim fanatics during the last decades have been labelled by politicians, religious leaders and scholars as a Clash of Civilizations. However, as the contributors to this book set out to explain, these acts cannot be considered an Islamic onslaught on Judeo-Christian Civilisation. While the hostile ideas, words and deeds perpetrated by individual supporters among the three monotheistic civilisations cannot be ignored, history has demonstrated a more positive, constructive, albeit complex, relationship among Muslim, Christians and Jews during medieval and modern times. For long periods of time they shared divine and human values, co-operated in cultural, economic and political fields, and influenced one anothers thinking. This book examines religious and historical themes of these three civilising religions, the impact of education on their interrelationship, the problem of Jerusalem, as well as contemporary interfaith relations. Noted scholars and theologians -- Jewish, Christian and Muslim -- from the United States, Canada, Egypt, Indonesia, Israel, Pakistan, Palestine and Turkey contribute to this book, the theme of which was first presented at an international conference organised by the Weatherhead Center for International Affairs, and the Divinity School, Harvard University.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".