Bibliographic record
Abstract
Johan Huizinga describes the waning Middle Ages as an epoch saturated with symbols and images. The numerous cathedrals and monuments from this period confirm the point, with their multitude of allegorical scenes depicted in murals and on pillars inside and out. The same can be said of those illuminated manuscripts so lavishly and carefully done that they transmit the message of the text on a pictorial level. During the Middle Ages, the image seemed to occupy a central role with regard to the perception of a given manuscript. In fact, the prestige of a manuscript was often tied to the quality and quantity of the illuminations and miniatures it contained, pointing to the wealth of the patron who commissioned it. The value and the popularity of a specific work can thus be judged, to a certain extent at least, by the number and the quality of its miniatures. A case in point, for instance, is the second manuscript "edition" of Christine de Pizan's collected works dating, as has been proven, from between 1410 and 1415, British Library, MS Harley 4431, prepared for Isabeau de Baviere, one of Christine's patrons. This manuscript is today one of the British Library's treasures, with twenty-nine individual works in prose and poetry, as well as one hundred and thirty miniatures of sumptuous quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".