Bibliographic record
Abstract
Dans ce court article, je brosse un bref panorama de ce que je considre comme les phases majeures du curriculum et de l'enseignement indiens, de la priode prcdant l'indpendance jusqu' l'poque actuelle. Il s'agit l d'une esquisse rapide d'un pays complexe, l'histoire complexe. Je n'ai pas dvelopp l'ide du rgionalisme, pas plus que je n'ai explor la question des peuples tribaux d'Inde, qui prsentent pourtant une srie de problmes et de questions dignes d'attention. Je n'ai pas non plus trait de la formation des enseignants. J'ai cherch en revanche prsenter un point de vue plus national . En matire ducative, j'ai essay de dfinir les ides cls -en termes de curriculum et de pdagogie -qui ont domin chaque priode. Loin de supplanter l'tape prcdente, chaque nouvelle phase s'ajoute la prcdente, ce qui fait que de multiples ides peuvent coexister et tre lgitimes un moment donn.
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".