The Formation of Medieval Territories in Mountain areas. A perspective from archaeology and written records at Caramulo (Lafões, central-northern Portugal)
Bibliographic record
Abstract
The research that has been carried out in the Lafões/Caramulo area (central-northern Portugal) has made it possible to acquire new data on the formation of medieval territories in a mountainous area. The present paper uses data from written documentation and archaeological data to make a first approach to the study of the of the formation processes of village territories and the different-scale sociopolitical processes that were behind these processes. The focus of this study are two medieval parishes which occupy the most mountainous areas of the present-day municipality of Vouzela. The available data show significant differences between the two, particularly in the configuration of the settlement and its dynamics over time. While in one the settlement areas are quite stable, the other records changes in the settlement structure, with new foundations and abandonments throughout the Middle Ages. As far as it is possible to understand, these differences are fundamentally correlated with the actors who played in each of these territories. In fact, it was social differences of local scales which were mainly responsible for these differences in the definition of territories, ownership of rural properties, size of plots, settlement patterns and surely socio-economic practices. Micro-scale politics have determined different histories and settlement features.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".