Semantically Interoperable Census Data: Unlocking the Semantics of Census Data Using Ontologies and Linked Data
Bibliographic record
Abstract
The Canadian Census of Population is a survey that collects statistical information on the Canadian population. These censuses contain valuable socioeconomic data that is often used by both the public and private sectors for project planning and decision-making. However, there are a few issues that may arise when using census data. Firstly, data wrangling, which is often a time-consuming process, needs to be conducted in order to clean and prepare the data for integration and use. Secondly, different datasets across different census years may be using different terms to describe the same concept/entity, hence creating a problem of referential equivalence (i.e., how do we know whether two different datasets are referring to the same concepts/entities?). Lastly, the data found in a census is often described using natural language that isn't easily interpreted by machines and can be difficult to break down or deconstruct. In this paper, we develop and propose the use of an ontology for representing the data from the Canadian Census of Population as linked data in order to address the aforementioned issues, evaluate the ontology using competency questions based on real world use cases, and discuss the advantages of census linked data for integration and visualisation uses.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".