Intelligence: What Is Indigenous to India and What Is Shared?
Bibliographic record
Abstract
The story is told of a discourse on mind, self, and intelligence that occurs in one of the seminal books of traditional knowledge, the Upanishad (Radhakrishnan, 1953). There is a learned man, Narada, who commutes between the land of gods and humans – he is dissatisfied in spite of his knowledge of books, which he teaches to humans; he wishes to know the nature of the self. He seeks out a wise man who is innocent of scriptural and book knowledge but is virtuous and practices love for all; this man is truly innocent like a five-year-old child. But he knows about the self. Narada asks him for lessons on self-knowledge, because he has heard that those who have such knowledge live beyond sorrow, and Narada says he is sad because he cannot cross over to the other side of sorrow! The wise man tells Narada to describe what knowledge he has already acquired, and then they can discuss how to go beyond it. NAME, SPEECH, AND MIND Narada's knowledge is vast – he knows the scriptures, mathematics and astronomy, medicine, warfare and weapons, the science of natural disasters, serpents, and the fine arts of dance and music. The boy-like wise man remarks that is good, you know the Name (declarative and procedural knowledge?), and as far as nomenclature goes, you should pursue it and be happy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".