Bibliographic record
Abstract
Performance measurement, monitoring, and \n evaluation have long been part of the infrastructure within \n the federal government in Canada. With more than 30 years of \n formalized evaluation experience in most large federal \n departments and agencies, many lessons can be gained, not \n the least of which is the recognition that the monitoring \n and evaluation (M&E) system itself is not static. The \n Canadian government has a formalized evaluation policy, \n standards, and guidelines; and these have been modified on \n three occasions over the past three decades. Changes have \n usually come about because of a public sector reform \n initiative such as the introduction of a results orientation \n to government management, a political issue that may have \n generated a demand for greater accountability and \n transparency in government, or a change in emphasis on where \n and how M&E information should be used in government. \n This chapter provides an overview of the Canadian M&E \n model, examining its defining elements and identifying key \n lessons learned.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".