Farmers in Delfzijl from the sixteenth to the early twentieth centuries::tenants and freeholders
Bibliographic record
Abstract
The chapter gives a general description of the quantitative development of agriculture over the very long run in the former municipality of Delfzijl (Groningen, the Netherlands), discussing the development of the number of farmers, farm-size, land ownership and the market price of agricultural land. Special attention is given to the fact that this region stands out in the province of Groningen for its relatively extremely large share of freeholders in the eighteenth century controlling up to 40% of the land. The chapter shows that these farms were not successors of medieval freehold farms. Nearly all large freehold farms came into existence in the second half of the seventeenth century or the early eighteenth century, when tenant farmers bought the ownership of land, usually from nobles or others belonging to elite groups. This was possible because of the extreme low prices in this period in the region, partly due to agricultural hardships related to persistent local water problems. Because of the low land rents and the high risks of extra costs for owners due to for instance the maintenance of dikes, investing in land around Delfzijl was no longer interesting. Consequently, a part of the more well-to-do local tenant farmers could buy their land for extremely low prices.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".