Bibliographic record
Abstract
Focussing on Christian performance of their liturgy, and Muslim recitation of their Qur'an, this chapter looks at more systematic and controlled ways of vocalizing the word “Jesus” and Jesus-related texts, in terms of factors like pronunciation, volume, breath control, and coordination with other peoples' utterances. Systematizing Jesus vocalization with regular rules created subtle connections accessible only through the deep ken. Some Muslims scholars, especially, inclined towards the plain ken did take into account human limitations and historical, cultural particularities, as with Al-Suyuti's interest in the Bedouins. Examples treated in the chapter include the sequence Victimae Paschali Laudes, which outside of its liturgical context was sung while performing a ballgame-dance at Easter in France. Polyphony was explicitly linked to Jesus, who was the only person able to speak and sing polyphonically simultaneously. Deep-ken meaning was brought into polyphonic liturgical music, especially through the use of a cantus firmus and through mathematics. Josquin's masses (especially those built upon the secular tune “L'homme armé”), among others, numerically encoded Jesus references. Both polyphony and its use of secular melodies provoked condemnation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".