Provincial Data-linkage to Address Complex Policy Challenges
Bibliographic record
Abstract
IntroductionThe Province of British Columbia, Canada has established a Data Innovation Program (DI Program) and a Data Science Partnerships Program (DSP Program) to use integrated public-sector data to drive insights into complex policy challenges and support the public good. These programs are a part of the province's new Integrated Data. Objectives and ApproachThe DI Program was built to enable policy decisions based on a more complete picture of the citizen journey across and throughout government programs. It provides a privacy and security framework for corporate data analytics and a cross-government secure research environment. The DSP Program provides analytics and/or project support for high-priority cross-government projects. The opportunity afforded by this approach to policy decision-making is that valuable data and evidence from multiple sectors can be utilized to make positive changes in the lives of citizens. ResultsThe IDO has partnered with cross government experts on a series of pilot projects that used linked data spanning social services, families and households, education, and health and clinical records. Research topics ranged from the prediction of risk of long-term unemployment, to the impact of the foreign home buyers tax, to the effectiveness of labour market programs. Throughout our presentation we will use these projects as case examples to address the benefits and opportunities provided through our citizen-centred, integrated approach. Conclusion/ImplicationsThe future of policy decision-making in terms of service delivery relies on mutually beneficial collaboration and the evidence-based insight available through integrated data. Moving forward, it is essential that researchers across government make the most out of integrated population-level data to solve pressing issues affecting the lives of citizens.
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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 | 0.044 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.025 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".