PRDH and IMPQ 1800–1849 Quebec Historical Family Reconstitution.
Bibliographic record
Abstract
Since 1966, the Programme de recherche en démographie historique (PRDH) has worked to create comprehensive genealogical data of the Quebec population. The PRDH longitudinal database, the Registre de la population du Québec ancien (RPQA), draws upon the French Catholic parish registers of the St. Lawrence Valley as its main source material. This family reconstitution covers the French Catholic population of Quebec up to 1799, along with deaths after 1800 of persons born before 1750. Subsequent partnerships with l’Institut Généalogique Drouin, FamilySearch and Ancestry as well as collaboration on the 2011–2017 Infrastructure intégrée des microdonnées historiques de la population du Québec (1621–1965) (IMPQ) project enabled the PRDH to continue efforts to reconstitute the French Catholic population up to 1849. Despite these advances, pushing family reconstitution forward to the mid-19th century has forced the PRDH team to reckon with the increasingly mixed and geographically mobile Quebec population of the 19th and early 20th centuries. This article describes the content and design of the RPQA database, detailing the structure of the RPQA relational database and the breadth of variables available for data management and analysis. It then describes features of the IMPQ extension of family reconstitution from 1800 to 1849, including observational protocols necessary to use these data and consideration of data completeness after 1800. At the same time, the article addresses the fundamental question, "what is my population?" as part of a broader reflection upon the target population encompassed by these data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".