Bibliographic record
Abstract
Terry Nardin’s chapter grows out of an experiment in cross-cultural teaching that has flourished at the Yale-NUS College in Singapore. The program crosses the civilizational boundaries that often define the way political theory is taught and refuses the usual distinction between “Western” and “non-Western” thought. This distinction privileges European thought, throwing the rest of the world into a residual category, obscuring the fact that the rise of “the West” brought much of the world under the dominating rule of Europeans. Drawing on ancient and modern texts from India, China, and the Islamic world as well as from Europe, the course combines the close reading of both political and philosophical texts. By drawing on a wider range of texts than is usual in political theory courses, it invites students and teachers to explore a diversity of genres and their associated contexts, presuppositions, and reverberations. It addresses disputes about canons, relevance, translations, and expertise, inviting students to engage with a broad intellectual inheritance. It illustrates how faculty can educate one another as they teach all the students in a liberal arts college how to transcend parochialism by sharpening their capacities for philosophical and political reasoning.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".