Bibliographic record
Abstract
Lo trobar reven, navèm de besonh! Vaici mai sirventes e tenson e cançon … Massilia Sound System, Chourmo! After maintaining eight centuries of interest, it does not seem that troubadour and trouvère music will loosen its hold anytime soon on professional or dilettante imaginations. The survival of this music as both cultural lore and object of scholarship implies a certain durability in the future. At least for a few more years after this book is published, and hopefully many more to come, people will be singing, speaking and writing about the songs of the troubadours and trouvères. Theirs is an ongoing reception to which I will return at the end of this chapter. This continuing reception has evolved over eight centuries of persistent curiosity. So it seems appropriate, even important, to summarize the fluid shape of this reception which I have detailed over the last five chapters. It offers a lesson, at times even a model for the maintenance of other older repertoires, and equally a lesson in their ontology. A good deal of earlier music cannot boast such a consistent profile over time; indeed, the survival of much old music is hardly guaranteed.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.310 | 0.075 |
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".