Rank: Statistics Canada (2023-03-16). Canadian Census of Population, 2021: Census Profiles: Commuting | Total - Time leaving for work for the employed labour force aged 15 years and over with a usual place of work or no fixed workplace address - 25% sample data | Total - Time leaving for work for the employed labour force aged 15 years and over with a usual place of work or no fixed workplace address - 25% sample data | Between 5 a.m. and 5:59 a.m. | Between 6 a.m. and 6:59 a.m. | Between 7 a.m. and 7:59 a.m. | Between 8 a.m. and 8:59 a.m. | Between 9 a.m. and 11:59 a.m. | Between 12 p.m. and 4:59 a.m. | Counts | Total | Men+ | Women+, 2021. Sage Data. Sage Publishing Ltd. (Dataset). Dataset-ID: 075-005-010
Bibliographic record
Abstract
Shows statistics on the location of one's workplace, main mode(s) of commute, travel time, and time leaving for work (based on their commuting experience from May 2 to May 8, 2021). Statistics presented include counts and rates, and are provided in total and by gender, where available. Due to the COVID-19 pandemic, early data collection practices in the more remote or isolated areas of Canada had to be revised and adapted for health and safety reasons. More detail and information can be found in the data dictionary located in the technical documentation.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.149 | 0.115 |
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".