Identifying Households for Historical Censuses to Generate Longitudinal Data
Bibliographic record
Abstract
The availability of historical censuses and advances in automatic record linking techniques provide social scientists and historians with research opportunities based on longitudinal data. Automatically linking the same individuals and households in multiple sources creates longitudinal data more quickly and with less effort. The most common way to do this is to link individual records (pairwise linkage). More recently, a strategy of linking groups of records has been used. Unfortunately, in some historical censuses, household identi ers (HID) were not recorded at the time of the enumeration or not transcribed into the digital collections. In this thesis, we link four Canadian historical censuses (1871, 1881, 1891, and 1901) using both pairwise and group-linkage methods. We develop and implement a method to identify HID in the 1891 and 1901 censuses automatically. Then, we use this new information to generate longitudinal data that follows 159,872 Canadians over three decades from 1871 to 1901.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".