Surnames in south-eastern France: structure of the rural population during the 19th century through isonymy
Bibliographic record
Abstract
Abstract An analysis of the distribution of surnames through time and space allows us to understand the structure of human groups, their exchanges or even their possible isolation. The French population has already been studied through surnames and it has been shown that the Sud-Provence-Alpes-Côte d’Azur region differed from the rest of France in both the 20th and 21st centuries (Mourrieras et al., 1995; Scapoli et al., 2005). The objective of this study was to understand the population evolution and particularities of the Sud-Provence-Alpes-Côte d’Azur region through an analysis of the distribution of surnames over an earlier period: the 19th century. For this work, 806,069 birth records from 521 communes between 1810 and 1890 were recorded and a total of 23,340 surnames were collected. The estimation of various isonymic parameters has allowed a description of this corpus never exploited before. In order to appreciate the population evolution, the data set was divided into three periods of 25 years. The canton was the geographical unit of this study, and similarities and differences between each of them were evaluated using Lasker distances, which allow the construction of dendrograms. A positive and significant correlation (p<0.0001) was found between Lasker distances and geographical distances using the Mantel test. The lowest inbreeding estimates were found in the Durance Valley. Migration, estimated from the v-index of Karlin and McGregor (1967), showed higher values in the south-western quarter of the region. The decrease in Rst values across the three periods is consistent with a homogenization of the patronymic between the cantons. This three-period approach showed a population evolution influenced by linguistic, cultural, historical and migratory phenomena since the Middle Ages, disrupted by the socioeconomic changes of the 19th century.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".