Spanish Landscapes in the Middle Ages: Reconstructing Territorial Memory from Early Documents and Cartography –A GIS-Based Methodology
Bibliographic record
Abstract
The gradual disappearance of traditional ways of life in the countryside is changing the historical relationship between rural inhabitants and their environment. As a consequence, the reconstruction of historical landscapes and territories plays an important role in historical memory. Toponyms are now considered an essential means of constructing community identities, because they refer to old uses and infrastructures, as well as to historical landmarks. Following the clues left behind by toponyms, old landscapes emerge from the past and endow the present ones with new meanings. The main goal of our project has been to preserve, protect, and disseminate this essential cultural heritage, which might otherwise sink into oblivion under the weight of the recent sweeping transformations wrought on the countryside by urban development and land use planning. To this end, we have developed an Open GIS-based tool that can be applied worldwide and which falls within the framework of European Union policies to promote free multilingual digital access to Europe's cultural heritage. This tool will provide free information on toponyms, helping to reconstruct historical territories and landscapes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".