Canadian Census Data Inventory, 1666-2021
Bibliographic record
Abstract
The Canadian Census Data Inventory is a project of the Canadian Census Data Discovery Partnership (CCDDP). The CCDDP project aims to facilitate historical research by reducing the barriers to access and use of Canadian census data. Using the search tab of The Census Data Discovery Portal users can discover historical and contemporary Canadian census materials and data items held by numerous institutions across the country. This dataset contains the original inputted and captured census items and corresponding metadata fields for the inventory tables in English and French including enhanced census terms for finding census data. Additional improvements are being made to the inventory and will be addressed by future infrastructure. Censuses, or population counts, have been conducted in the territory now known as Canada since 1665-66 in New France. Canada’s census is our most valuable primary economic, social, and cultural data set, and is an essential research tool for the formation of new knowledge and understanding about the populations that lived here in the past and present. The sources of information that make up these censuses are rich, diverse, and complex. They consist of databases of archival records, publications, and data files, containing a mix of primary data, expert analyses, and supporting documentation. This project is focused on those sources that contain data, and documentation that supports the use of the data. The CCDDP project is funded by a SSHRC Partnership Development Grant (2021-2024). Key deliverables are inventorying Canada’s historical census data, the design and delivery of this proof-of-concept bilingual discovery portal, and developing a set of recommendations for future work. Please see the project website for the full list of project partners, investigators and collaborators, and join our listserv to receive project updates. Inventory data last updated: 2024-03-31
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.033 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.102 | 0.057 |
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".