Replication Code for: Differences in site-specific cancer incidence by individual- and area-level income in Canada from 2006 to 2015
Bibliographic record
Abstract
The files included here are SAS code provided as documentation for reproducibility of results in the study: Tope P, Morais S, El-Zein M, Franco EL, Malagón T. Differences in site-specific cancer incidence by individual- and area-level income in Canada from 2006 to 2015. Int J Cancer. 2023 Nov 15;153(10):1766-1783. doi: 10.1002/ijc.34661. Epub 2023 Jul 26. PMID: 37493243. https://doi.org/10.1002/ijc.34661 Data Source: Statistics Canada, Canadian Census Health and Environment Cohorts 2006 & 2011, 2006 long-form census, 2011 National Household Survey, Canadian Vital Statistics Death Database 2006-2015, and Canadian Cancer Registry 2006-2015. The Postal CodeOM Conversion File Plus (7D) is based on data licensed by Canada Post Corporation. Reproduced and distributed on an "as is" basis with the permission of Statistics Canada. This does not constitute an endorsement by Statistics Canada of this product. Statistics Canada is the owner and steward of the data used in this report, and access to the data is regulated by the 1985 Statistics Act. To access the data, researchers must become deemed employees of Statistics Canada and sign a research contract. Members of post-secondary institutions such as faculty, students, or staff may apply for data access to Statistics Canada microdata through the Research Data Centre program using the Microdata Access Portal (https://www.statcan.gc.ca/en/microdata/data-centres/access).
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 | 0.018 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.179 | 0.058 |
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".