Heroes, Huddles, and Habitats: Enabling Big Data for Advanced Analytics at the Government of Canada
Bibliographic record
Abstract
The recent proliferation of big data for advanced analytics (BDA) in the Government of Canada (GC) is emerging as an opportunity with potential for reshaping how the GC conducts operations in its core functions: policy analysis, public administration, and service delivery.Nonetheless, there remain notable barriers that make the integration of BDA difficult in many areas within the GC.However, there is a clear dichotomy between projects in the GC that fail and those that succeed at integrating BDA as a transformative change force.This thesis Amanda Clarke for her guidance, unwavering support, and invaluable insights throughout the duration of this thesis.Her expertise, patience, and encouragement have been instrumental in shaping the direction of this research and fostering my intellectual growth.I am profoundly thankful to the members of my thesis committee, Professor Leslie Pal and Professor Robert Shepherd, for their constructive feedback, contributions and commitment to enriching the quality of this work.Your subject matter expertise was invaluable.I am deeply grateful to my family, especially my parents
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".