Levnadsstandarden speglad i bouppteckningar : En undersökning av två metoder att använda svenska bouppteckningar för en levnadsstandardsundersökning samt en internationell
Bibliographic record
Abstract
The standard of living in the pre-industrial world is an interesting but challenging\nsubject. No single method alone can solve the problem. In order to grasp the standard of living\nseveral methods must be used, and work must be done across discipline borderlines. In this\npaper I discuss methods for using post mortal inventories (estate inventories) in order to indicate\nthe material standard of living in Sweden. Similar methods have been used in France, Great\nBritain and North America. To adapt these methods to Swedish conditions have been the main\nfocus of the paper. The first method tested here uses the valuation of all objects stated in the\npost mortal inventory (not including houses and farms). This method had previously been tested\non English and North American inventories. All values in the Swedish material have been\nconverted to British pounds in order to facilitate comparisons. The second method tested uses\nthe objects in the estate inventory to create an index of the material standard of living. This index\nwas first used in France and later in Canada. Some preliminary results in this work indicate that\nSwedish farmers by 1750 were well below English and French farmers in material standard of\nliving. However, during the fifty years that followed a noticeable change took place.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.092 | 0.045 |
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".