Bibliographic record
Abstract
The polygon is the most common vector data model used to represent political entities in spatial history and historical GIS. When it comes to visualizing the entangled, complex political geography of early modern societies, however, discrete, contiguous features can be a problematic cartographic choice. One example explored in detail here is the aggressive enserfment of foreign peasants by the Electoral Palatinate in the seventeenth-century Holy Roman Empire. The comparison of an older map of these events with GIS maps based on new data shows how the polygons on the older map exaggerate the extent of Palatine expansion and suggest a continuous distribution of phenomena that were really discontinuous. Indeed, in early modern political geography, the polygon often operates as the cartographic equivalent of problematic concepts such as absolutism and sovereignty. Though point-based maps offer more accurate representations of pre-modern spatial orders, they hinge on the availability of geospatial data. Discussing the potential and limitations of different kinds of spatial data available to early modern historians today, the conclusion calls for a more argument-driven spatial history of early modernity.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".