The Canadian Statistical Demographic Database Research Project: Exploring Potential Use of Administrative Data to Support the Canadian Census Program
Bibliographic record
Abstract
ABSTRACT ObjectiveStatistics Canada initiated the Canadian Statistical Demographic Database (CSDD) research project to determine if and how administrative data could be used to support the Canadian Census Program. The project’s goal is to create a census spine from administrative data sources. The CSDD’s current scope is limited to basic information (name, sex, birth date and usual place of residence) for all Canadians. MethodTwo 2011 CSDD prototypes were built using and linking hundreds of administrative files obtained mainly from other federal departments. Extensive pre-processing activities must take place prior to linkage to remove duplicates and standardize file variables. Given that Canadians do not possess a single unique identifier, administrative files were linked using record linkage methods; key matching variables were identified, validated and used to perform the linkage. This work led to the development of auxiliary files, which serve specific purposes related to the CSDD development. They also provide useful linkage keys to other Statistics Canada statistical programs.ResultsThe outcome of the CSDD is determined by comparing it to two references. First, comparisons were done at the aggregate level (Canadian, provincial and sub-provincial levels) by contrasting the results with Demography Division’s official population estimates for the 2011 Census. The CSDD was also compared with the 2011 Census of Population’s Response Database (RDB), which allows for analysis at the micro (record) level. The RDB contains non-imputed data on name, sex, birth date and usual place of residence as provided by individual census respondents. Comparisons with the RDB have allowed us to address the question, “Does the CSDD put the right person at the same address as the 2011 Census does?” Results are promising. At the aggregate level, the CSDD compares well with the demographic estimates for the 2011 Census at the national, provincial/territorial and some urban area levels. At the micro level, the CSDD contains more individuals than the RDB. Improvements are needed with regards to its ability to place persons accurately in rural areas due to the lack of good residential addresses in administrative data files. Initial results led to the planning of new CSDD prototypes, this time for 2016, in line with the 2016 Census of Population.ConclusionThe presentation will give an overview of the methods and principles behind the construction of the CSDD. Basic analytical results will present areas of strength and weakness. Lessons learned and upcoming challenges along with their proposed solutions will complete the presentation.
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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 | 0.043 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.041 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".