Quantifying Historical Migrations Using a Multi-Step Probabilistic Algorithm and Surname Distributions over the Centuries: A Case Study of Malopolska
Bibliographic record
Abstract
Contemporary studies on historical migrations gradually demystified the idea that the population in the past was stable and homogenous, and unveiled dynamic and complex connections throughout the whole of Europe. However, even if we are in the era of digitalization and big data, there is no standardized methodology to investigate historical migrations, and there are major challenges in quantifying and providing consistent estimates of migration groups. This research aims to tackle these challenges by proposing a replicable approach, consisting of a multi-step algorithm that uses probabilistic analysis to quantify migration and estimate the most likely migrants’ origin based on surname distributions over the centuries. Particularly, the focus is on the Malopolska case study, on both international and Polish migrations to the region from the 1500s to the Great War. The proposed approach to quantification of historical migrations can be a helpful tool to use in combination with existing methodologies to validate and increase the accuracy of the estimates of other case studies in Europe. Les études contemporaines sur les migrations historiques ont peu à peu démystifié l’idée selon laquelle la population passée était stable et homogène, et ont mis en lumière des connexions dynamiques et complexes à l’échelle de l’Europe entière. Cependant, même à l’ère de la numérisation et des mégadonnées, il n’existe pas de méthodologie standardisée pour étudier les migrations historiques, et il reste de grandes difficultés à quantifier et fournir des estimations cohérentes des groupes de migrants. Cette recherche vise à relever ces défis en proposant une approche reproductible, fondée sur un algorithme en plusieurs étapes qui utilise une analyse probabiliste pour quantifier la migration et estimer l’origine la plus probable des migrants à partir des distributions de noms de famille au fil des siècles. L’étude se concentre en particulier sur le cas de la Malopolska, en analysant les migrations internationales et polonaises vers cette région, du XVIᵉ siècle jusqu’à la Grande Guerre. L’approche proposée pour la quantification des migrations historiques peut constituer un outil utile, à combiner avec les méthodologies existantes, afin de valider et d’affiner les estimations d’autres études de cas en Europe.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".