Des dieux qui bâillent et qui font bâiller dans la mythologie épique de l’Inde
Bibliographic record
Abstract
Souvent relié au sommeil, le bâillement s’est prêté en Inde ancienne aux interprétations les plus diverses. Mais, dans un contexte mythologique où le sommeil de la divinité implique la destruction des mondes, le bâillement annonce souvent un tel bouleversement. Il arrive également qu’on l’utilise en dernier recours comme une arme redoutable pour vaincre un adversaire coriace. Des mythes expliquent l’origine du bâillement soit comme une invention des dieux pour venir à bout du terrible Vrtra, soit comme une force de destruction liée à la fièvre et au dieu Rudra, soit encore comme une force relevant de la déesse Yoganidrā. Même s’il apparaît relativement souvent dans les récits de mythologie hindoue, le bâillement n’a pourtant jamais à ma connaissance été étudié pour lui-même et c’est la tâche que se propose le présent article. Often related to sleep, yawn has been interpreted in a variety of ways in ancient India. But in the mythological context of the epics, yawn often presages the ensuing destruction of worlds which occurs when god Nārāya a falls asleep. Yawn is also employed as a weapon of destruction used by the highest gods and asuras against particularly difficult adversaries. One myth explains the existence of yawn as a creation of the gods used to destroy the terrible Vrtra; a second myth describes yawn as a destructive power linked to the presence of Fever (jvara) and to the god Rudra; a third myth links its power to the goddess Yoganidrā. Despite the fact that it occurs with some frequency in Hindu mythological narratives, yawn has never been studied in its own right, a situation which this paper intends to correct.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".