A comparison of machine learning classifiers for use on historical record linkage
Bibliographic record
Abstract
Record Linkage is the process of identifying the same entities in one or more data sources in the absence of unique identifiers. Longitudinal data constructed by linking two or more historical sources can provide us with valuable information about the characteristics of population change over time. However, the construction of such longitudinal data is challenged by the unavailability of personal identifiers. In this thesis, we link the Canadian censuses 1871 and 1881 using different methods and conditions. The Support Vector Machine and the Random Forest Classifiers are used to establish the record linkage system. The performance of these different methods is compared to investigate the upper bound achieved in the linkage rate and the conditions which provided us with that rate are inspected. Experiments show that the Random Forest classification explored in this thesis improves upon the linkage rate by 3.6% while maintaining a false positive rate no greater than 5%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".