Statistic Canada’s Longitudinal Social Data Development Program (LSDDP)
Bibliographic record
Abstract
IntroductionStatistics Canada has a long and reputable history of data linkage and an established Social Data Linkage Environment (SDLE). Under the agency’s modernization agenda the Longitudinal Social Data Development Program (LSDDP) exemplifies the agency’s efforts to position linkable administrative data as central in the field of social statistics. This is in response to the call for better longitudinal and intersectional social measures in an increasingly complex society. Objectives and ApproachThis presentation will include a detailed description of the LSDDP’s research and development activities which are centered on a linkable pseudonymised administrative data-first approach to social measures in the areas of longitudinal life-course analysis and intersectional social measures. The approach builds on existing activities flowing out of the Social Data Linkage Environment (SDLE), but with a more systematized, deliberate, and replicable approach; a set of analytical tools and processes. ResultsThe presentation will describe the following aspects of the LSDDP’s work: Responsible research and development under the principles of necessity and proportionality Data development and data structure Intersectional social indicators Methods and applied research in life-course analysis Conclusion / ImplicationsThe LSDDP presents a seismic opportunity responding to the need for holistic and multi-dimensional measurement of society that considers social progress and well-being as it relates to the interrelationships over time among social domains. In absence of longitudinal survey data such approach enables the staying upstream of social issues as well as the identification of intervention points for programs, policies, and other initiatives.
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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 | 0.034 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.026 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.020 |
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".