Family, farm, and factory: Labor and the family in the transition from protoindustry to factory industry in 19th-century Twente, the Netherlands
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».