The Data Aren't Alright, Or: How I Learned to Stop Worrying and Love the Archives
Bibliographic record
Abstract
The settlement of the Canadian prairie provinces in the late 19th and early 20th centuries was shaped by waves of immigration, including significant numbers of Ukrainians seeking new opportunities. Understanding the early settlement patterns of Ukrainians is a challenging task, particularly because of the inaccuracy of ethnic origin data in historical Canadian Census of Population records. This challenge is due in part to the unique political and historic circumstances of Ukraine during major periods of Canadian immigration. These factors complicate efforts to accurately trace the origins of settlers using traditional sources of demographic data. Archival documents, such as homestead records and township maps, contain more accurate place-of-origin data, but they are harder to access because of inadequate digitization. Homestead records include hand-written information about naturalization and citizenship status as well as the date of arrival on the homestead. This information has been partially transcribed through a community initiative, but the database is incomplete and not machine-readable. Township maps contain handwritten names and geographic locations of settlers, but they have not been widely digitized and are accessible only in provincial archives. This presentation will address the limitations of historical census data in capturing the ethnic origins of early Ukrainian-Canadian settlers and highlight the importance of archival research in reconstructing histories that are obscured by systemic inaccuracies in official records. This work is part of a larger program of research investigating the spatial and social dynamics of Ukrainian-Canadian settlement in Canada.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.027 |
| Insufficient payload (model declined to judge) | 0.039 | 0.024 |
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".