Bibliographic record
Abstract
Teaching Plato in Palestine is part intellectual travelogue, part plea for integrating philosophy into our personal and public life. Philosophical toolkit in tow, Carlos Fraenkel invites readers on a tour around the world as he meets students at Palestinian and Indonesian universities, lapsed Hasidic Jews in New York, teenagers from poor neighborhoods in Brazil, and the descendants of Iroquois warriors in Canada. They turn to Plato and Aristotle, al-Ghaz?l? and Maimonides, Spinoza and Nietzsche for help to tackle big questions: Does God exist? Is piety worth it? Can violence be justified? What is social justice and how can we get there? Who should rule? And how shall we deal with the legacy of colonialism? Fraenkel shows how useful the tools of philosophy can be-particularly in places fraught with conflict-to clarify such questions and explore answers to them. In the course of the discussions, different viewpoints often clash. That's a good thing, Fraenkel argues, as long as we turn our disagreements on moral, religious, and philosophical issues into what he calls a "culture of debate." Conceived as a joint search for the truth, a culture of debate gives us a chance to examine the beliefs and values we were brought up with and often take for granted. It won't lead to easy answers, Fraenkel admits, but debate, if philosophically nuanced, is more attractive than either forcing our views on others or becoming mired in multicultural complacency-and behaving as if differences didn't matter at all
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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 | 0.004 | 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 | 0.013 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".