Bibliographic record
Abstract
On 3 June 1449 king René inaugurated the Tournament of the Shep¬herdess at Tarascon in Provence. To the sound of trumpets and tam¬bourines the king and queen of Sicily mounted a scaffold close to the lists. They were accompanied by their son-in-law, Ferry of Vaudemont, by great Angevin nobles, such as Guy de Laval, lord of Loué, and Louis de Beauvau and many Provençal knights. A detailed account survives in a poem by Louis de Beauvau. 1 The part of the shepherdess, who was to distribute the prizes, was played by Isabelle de Lenoncourt a noble lady of Lorraine. She wore a grey damask robe in a pastoral style and a red hat and carried a silver crook. She entered on the first day on horseback escorted by one of the judges of the tournament and the king of arms, her sheep followed with the two men who actually looked after them, and she was installed in a rustic bower decorated with flowers. Two shields hung from a tree nearby, one was white for joy the other black for sorrow. Two knights, ‘the shepherds’ Philippe de l’Aigue, René’s chamberlain, and Philippe de Lenoncourt (probably the father or brother of ‘the shepherdess’) defended them against the eighteen other contestants as each attempted to touch one of the shields. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.416 | 0.111 |
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".