Scultura altomedievale e analisi delle corrispondenze: l’atelier Piemontese-Provenzale (o la ‘Bottega delle Alpi Marittime’ quarant’anni dopo)
Bibliographic record
Abstract
In this paper, the statistical method of the correspondence analysis - already abundantly employed on several classes of archaeological finds - is applied, for the first time, to the Early Medieval church architectural sculpture, which is mainly found in fragments, owing to later reuse as building material. The correspondence analysis, in particular the seriation, allows to study the sculpted fragments in a systematic way, and to achieve solid and verifiable typological grouping. The typological grouping is extremely useful to ascertain the relative and absolute chronology of large sets of sculpted stone items, but can also be exploited for detecting, characterise and distinguish from one another the sculpting workshops operating in a given macro-region during a certain period. The ‘sample’ chosen for performing the seriation is a very significant group of sculpted fragments from nowadays South-Eastern France and North-Western Italy, which are not only particularly suitable to this kind of research, but also required a global, comprehensive reassessment in the light of the advancement of knowledge in archaeology and history in the last decades. Among other results, the study led to the identification of an itinerant group of sculptors who worked in thirteen locations towards the last quarter of the 8th century.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".