Looking West to India: Asian education, intra-Asian renaissance, and the Nalanda revival
Bibliographic record
Abstract
Abstract More than 800 years ago—at approximately the same time as the founding of the first European universities—the renowned monastic institution known as the Nālandā Mahāvihāra disappeared from historical records. Since 2006, a transnational Asian initiative to revive ancient Nalanda as ‘Nalanda University’ in Bihar, India, has been embraced at the highest government and philanthropic levels by a consortium of South, Southeast, and East Asian nations. Nalanda, described as an ‘icon of Asian renaissance’, and the issues surrounding its revival raise important questions about how a new interest in ‘pan-Indo-Asianism’ and a newly imagined vision of ‘Asian’ education are seen as converging to promote Asian interests. First, I consider the ambivalent relationship of the revival and its pre-modern namesake against the Nālandā Mahāvihāra's known history. Then I characterize two kinds of discourse on the contemporary project: one that is ‘pan-Indo-Asian’ and frames the revival as serving transnational Asian goals; and another that is Indic and imagines Nalanda as advancing Indian national concerns. While, for the various stakeholders, serious fissures are evident in the symbolic values of Nalanda—as an exemplar of Asia and of India—both types of discourse, taken together, reveal important insights into the development of an alternative model of education that is both modern and ‘Asian’.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.008 |
| 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".