Bibliographic record
Abstract
Digital technologies can now make an innovative contribution to urban history, lending themselves to processes aimed at achieving quite different goals, from research to training, from the dissemination to the fruition of cultural cartographic heritage. Through technological innovation and the development of multimedia tools, it is now possible to integrate traditional knowledge with alternative means of communication that can foster new methods of research, e-learning, and other potential uses of information and communications technology (ICT) for cultural and educational uses. The research reported herein is part of a more extensive project aimed at studying and visualizing how cities change over time. It focuses, in specific, on the Venice Arsenale (Italy) and develops the combined use of database archiving (DB) with geographic information systems (GIS) to (1) manage and georeference historical data, (2) model key phases of urban development on base maps, and (3) produce renderings and pertinent multimedia materials for the widespread dissemination of historical data to a highly diversified public. These operations were performed with two specific goals in mind: on one hand, to provide new research tools that might open to new knowledge and further enhance the city's history by representing its transformation over time; on the other, to investigate the possibilities of bringing historical research together with today's communication and multimedia distribution strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.012 |
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".