Arabic and its Alternatives: Religious Minorities and their Languages in the Emerging Nation States of the Middle East (1920-1950)
Bibliographic record
Abstract
but at the same time could (and were) put to work to create distinction and difference.Two dissertations, by Sasha Goldstein-Sabbah and by Tijmen Baarda, provide in-depth studies of two of the three case studies, while a monograph on the Catholics of Palestine by Karène Sanchez is on its way.The current volume and its introduction conclude the project, although the material is far from exhausted.We sincerely hope that others will continue to add examples, compare with what is here, criticize our conclusions and continue the discussion about how to understand the complicated and sometimes violent interactions between majorities and minorities, between Muslims and non-Muslims, and between Arab speakers and non-Arab speakers in the Middle East.Finally, the fact that many of the encounters and conflicts that we discuss in this volume assume significant Western influence -through missionaries, colonialists and others -should remind us that we are not speaking about an isolated episode of Middle Eastern history, but about a history that should be part of European and American historic consciousness as much as it is of those who are born and raised in the Middle East.We hope that our thinking, reading and writing about it will contribute to an increased sense of shared history.This creation of a shared history includes the analysis of painful episodes in which Western and Middle Eastern majorities were quick to sideline and sometimes erase the voices of minorities in order to advance particular rather than common interests.We hope that this volume, to which authors from many different countries and many different academic, linguistic and cultural contexts have contributed, may serve to re-read and re-appropriate this shared history, not to offer a final conclusion, but to stimulate discussion and ongoing reflection on how different kinds of people may live together, in the Middle East as much as in Europe or anywhere else in this world.At this time and place, I would like to thank all those who contributed to the project over the past years.The most important of these has been Karène Sanchez who contributed in innumerable ways: she was vital in conceptualizing and developing the project, in writing the research proposal and contribute her own post-doc research.She also took a big share in supervising our two PhD candidates, especially after I took on a position at Nijmegen's Radboud University in the summer of 2015 and thus no longer was available for day to day supervision in Leiden.The two PhD candidates, Tijmen Baarda and Sasha Goldstein-Sabbah, also have been a great help in pushing the project forward -through their own research and the questions that arose from it, and through their help in organizing conferences and publishing our results.During both conferences we were supported by many different people, but especially by -978-90-04-42322-0 Downloaded from Brill.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".