Muséaliser l’archéologie : quelques interventions innovantes pour la présentation des ruines / Musealizzare l’archeologia: alcuni interventi innovativi per presentare le rovine
Bibliographic record
Abstract
Intervention on archaeological sites requires a multicriteria approach, which does not lend itself to providing solutions of a general nature so much as solutions linked to the individual case at hand and to the comparison of the interventions involved in the creation of a museum-like context for them. Nevertheless, one should not lose sight of the general aspect of the problem and underestimate a series of considerations regarding what to conserve and why in favour of how to conserve, an aspect which has been extensively tested and which today offers a large range of appropriate solutions and techniques. It is a question of communication, solutions to which must be provided both through tools typical of architecture and through the ability to devise tools that are more characteristic and typical of museography. A number of possible communication strategies of archaeology will be set out and applied to the case study under consideration, that is the domus A and B (insula I) of the Hellenistic-Roman Quarter of Agrigento.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".