Census of Population, 2001 [Canada]: Topic-based Tabulations, Language Used at Work [B2020]
Bibliographic record
Abstract
Topic-based Tabulations paint a portrait of Canada based on various topics, that is on groups of variables on related subjects. They are available for various level of geography. Some tables provide a simple overview of the country; others consist of three or four cross-tabulated variables; and will others are of special or analytic interest. The topic-based tabulations are categorized into 3 data products listed below: Canadian Overview Tables (COT): A Profile of the Canadian Population, Where We Live Basic Cross-Tabulations (BCT), and Special Interest Tables (SIT). Some Topic-based Tabulations are accessible on the official day of release of the variables. Other tables are added to each topic through the course of the dissemination cycle. Users have access to progressively more detailed cross-tabulations and more detailed levels of geography. The Topic-based Tabulations replace the former series The Nation, Dimensions and Basic Summary Tables.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.035 |
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".