Bibliographic record
Abstract
In Canada, the concept of monitoring and evaluation (M&E) is interpreted such that evaluation has a distinct identity from monitoring. The Canadian M&E system is one that has invested heavily in both evaluation and performance monitoring as key tools to support accountability and results-based management. Section two of the paper traces the evolution of the formalized use of M&E in Canada's public sector from its origins in the 1960s to the present day. Section three outlines the organization of M&E, identifying the key players at a government-wide level, as well as M&E organization within an individual government department. Section four highlights the key features that define the Canadian M&E system, characterizing the system on the basis of eight distinguishing elements. Section 5 provides information on the ways that M&E information has been used in the Canadian public sector, including recent efforts to strengthen the link to decision-making. Lessons learned from the Canadian experience with public sector M&E are summarized in section six under three broad categories: lessons regarding drivers for M&E; lessons pertaining to the implementation of the M&E system and; key elements associated with M&E capacity building.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".