Family, farm, and factory: Labor and the family in the transition from protoindustry to factory industry in 19th-century Twente, the Netherlands
Bibliographic record
Abstract
In this article, sex and class-specific career paths in two 19th-century Dutch communities are investigated. The neighboring communities were both heavily involved in protoindustrial cotton production during the first half of the 19th century, whereas during the second half, one of the communities (Borne) became fully industrialized while the other (Wierden) hardly industrialized, and instead began to develop commercial agriculture. For both of these periods in both communities marriage cohorts were reconstituted, leading to a total of four reconstitutions. Based on data from marriage certificates in both communities, three occupational categories can be distinguished: those involved in agriculture, textiles, and other occupations. For all heads of households in each of these groups—and for their wives, whenever possible—life courses were reconstructed from the following data available in the civil registers: births and deaths of the children (if they died before reaching age 15) and deaths of spouses. A change of occupational category was not uncommon: Over one quarter of all heads of households made such a change at least once in their lifetimes. For women, this proportion was less certain, but clearly, it was not unusual for them to change occupational categories as well. Most changes were from the textiles to the agriculture category. At age 20–25 years, roughly a 40–40–20 division existed between agriculture, textiles, and other occupations; by age 65, that division had become roughly 80–0–20. Almost all men who started their careers in textiles shifted to agriculture over the course of their lives. Changes from agriculture to textiles were rare, whereas men working in the “other occupations” category (mostly small business, handcrafts, and services) hardly ever changed categories. This was true for both communities and both protoindustrial and industrial periods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".