Moving Ottawa's Department and Agency Reporting Forward: Encouraging Accountability and Sustaining Reform
Bibliographic record
Abstract
Abstract Governments produce a steady stream of data, reports, and other information to ministers, legislators, and the public, and generate enormous flows of administrative data. In the digital era the flows of information generated by departments and agencies have expanded by orders of magnitude. The paradox, though, is that much of this information is never used, but many decision makers believe there is insufficient information to meet their specific needs at any time. This note focuses on two streams of reporting by departments and agencies in the Government of Canada informed by “open government” principles—the streams of information supplied via the Canada InfoBase and other reporting, and the Management Accountability Framework—and considers if they are accessible and useful. After setting out options for making them both more useful, this note argues it will inform the work of spending reviews, Parliamentary committees, and engagement with experts and 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 | 0.127 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.043 | 0.015 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".