Accessing our past: the historical Census of Canada data inventory project
Bibliographic record
Abstract
The Historical Census of Canada Working Group is developing a bilingual inventory of Canadian Census data, and investigating how access to these resources might be provided through a single interface. Our vision is to eventually build an open, bilingual, Census of Canada research platform that would facilitate long-term access to print and digital census collections throughout Canada's history. The working group began as part of the Ontario Council of University Libraries (OCUL) but has now expanded nationally and is collaborating with partners across Canada to compile the inventory, which will include census products (data tables, maps, spatial data, documentation, and more) from all Canadian censuses going back to 1665. This session will outline the working group’s decisions about project scope, metadata framework, bilingualism, software tools and inventory processes, and provide a project status update. It will also provide an overview of the group’s vision for the Census of Canada research platform, and discuss how this project might improve access, usability and long-term preservation of census materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.036 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".