Bibliographic record
Abstract
Deficit quia deficiebat in exemplari. Troubadour chansonnier R, fol. IIIv, lower margin Quot sunt notatores, tot sunt novarum inventores figurarum. Walter Odington, De speculatione musice Sometime in the last three decades of the thirteenth century, two medieval scribes sat down to write the melody for the song ‘Pour conforter ma pesance’ by Thibaut IV count of Champagne and king of Navarre, then some thirty years deceased. The one we may call scribe T was writing in the Artois region of France while scribe O was located further south-west, most likely Burgundy or the Isle de France. Despite their geographic distance, these two readings are remarkably similar in pitch, something which we might expect given the relative closeness of these scribes to Thibaut's time. But this is not so for their rhythmic interpretations of Thibaut's melody. Scribe O, who has a decided tendency to interpret trouvère songs rhythmically by indicating long and short values, has here abstained from doing so (example 1.1), while scribe T, who elsewhere does not give rhythmic values, has done so in this case (example 1.2); his reading clearly alternates long and short values, creating a rhythmic pattern called a ‘mode’ ( modus ) by medieval theorists. For some reason, for this particular song, both music scribes decided to change their habits, switching rhythmic camps, so to speak.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.390 | 0.262 |
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".