Exploring population structure and migration with surnames : Quebec, 1621-1900
Bibliographic record
Abstract
This research uses isonymy (same-surname) methods and models to examine the population structure and migratory history of Quebec, Canada. Through a case study using 1765 and 1881 census and marriage records from 1621-1900, I explore the accuracy of sources as well as develop, test and apply different statistical methods, and experiment with mapping techniques that reveal paths and patterns of French Canadian surnames. Each investigation explores and evaluates a particular method. I noted that multivariate methods, including cluster analysis, relevance networks, and correspondence analysis, not traditionally used in surname analysis offer reliable and informative results, and insights into the hierarchical structure of populations not easily gleaned from traditional surname methods. The spatial and temporal components of Quebec surname distributions revealed that groups of names which populate and distinguish certain regions were in place by 1800, and cross-river relatedness became less significant as the population expanded upstream away from the St. Lawrence River. I also found that surnames unique to certain regions remained strongly clustered until the mid-nineteenth century when urbanization and the settlement of new territory led to the fusion of name pools (diversification) in and around urban areas, while at the same time causing losses of names in some rural areas. The marriage records provided evidence, through their measure of random mating, that surnames within different regions in Quebec continually diversified throughout the nineteenth century. Overall, I found surnames to be an informative variable for inferring population relatedness and migratory paths. Because surnames are readily available in a number of sources researchers involved with historical migration research should find that the methods presented in this work will provide a time-saving technique which can overcome the restrictions of spatial and temporal scale an
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".