Using linked data to inform multidimensional real-world issues: Canadian examples
Bibliographic record
Abstract
Issues at the top of global and national policy agendas, such as the COVID-19 pandemic, climate emergency, and energy and cost of living crises, are illustrating the interconnectivity of the economy, society, and the environment. Moreover, it is clear, that impacts of these issues are not uniformly felt across societies. Greater granularity of information is required to address inequalities. As a result, policy makers are taking a more holistic view of issues to address the interlinkages across domains. This is driving the demand for Statistics Canada to provide statistical insights that address these cross-cutting policy issues and provide more granular information across multiple domains. Linked data are necessary for understanding the interrelated nature of these issues and the scope of their impact. For example, recent Canadian research based on linked data has shown that COVID-19 led to changes in work arrangements that have implications for public transit use and greenhouse gas emissions. The employer-employee linked administrative data augmented with a further linkage to census data provide opportunities for Canadian researchers to produce multidimensional insights on a portrait of racialized groups and immigrants including refugees’ presence in the Canadian economy, their sociodemographic characteristics, and their performance over time relative to other Canadians, particularly post the COVID pandemic. Rapid growth in data availability for research also poses new challenges ranging from technical issues needed for the feasibility of data linkage to stewardship issues related to governance, access, and oversight. The presentation will also discuss the lessons learned and reflections from the Canadian experiences.
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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 | 0.064 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.043 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".