Bibliographic record
Abstract
1. Introduction: town and country in Europe, 1300-1800 S. R. Epstein 2. Town and country in Sweden, 1450-1650 Robert Sandberg 3. Town and country in Holland, 1300-1550 Peter Hoppenbrouwers 4. Town and country in the Dutch Republic, 1550-1800 Marjolein 't Hart 5. Town and country in England, 1300-1570 James A. Galloway 6. Town and country in England, 1570-1750 Paul Glennie 7. Town and country in the Polish Commonwealth, 1350-1650 Andrzej Janeczek 8. Town and country in the Austrian and Czech Lands, 1450-1750 Markus Cerman and Herbert Knittler 9. Town and country in Germany, 1350-1600 Tom Scott 10. Town and country in Switzerland, 1450-1750 Martin Korner 11. Town and country in France, 1550-1750 Thomas Brennan 12. Town and country in Castile, 1400-1650 Pablo Sanchez Leon 13. Town and country in central and northern Italy, 1500-1750 Carlo Maria Belfanti 14. Town and country in the Kingdom of Naples, 1500-1750 Brigitte Marin.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".