Processed 2021 Census of Population Data for Canada's Aggregate Dissemination Areas (Parquet)
Bibliographic record
Abstract
This dataset provides a processed Parquet version of Statistics Canada’s 2021 Census of Population data for the census subdivision geographic level in Canada. It is based on the 2021 Census Profile comprehensive download files published by Statistics Canada, specifically the aggregate dissemination areas (ADA) geography. An ADA is a dissemination geography created for the Census. ADAs cover the entire country and, where possible, have a population between 5,000 and 15,000 based on the previous census population counts. ADAs are created by grouping existing dissemination geographic areas, including census tracts (CTs), census subdivisions (CSDs) or dissemination areas (DAs). ADA boundaries respect provincial, territorial, census division (CD), census metropolitan area (CMA) and census agglomeration (CA) boundaries. The intent of the ADA geography is to ensure the availability of census data, where possible, across all regions of Canada. This release is part of the Data for Canada d4c-datapkg-statistical collection and is intended to support machine-readable, reproducible analysis using modern data tools and workflows.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.023 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.039 |
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".