Evaluation in the Provinces and Territories: A Cross-Canada Snapshot and Call to Action
Bibliographic record
Abstract
Abstract: Evidence-based decision-making and managing for results are terms often heard from politicians and senior government officials at both federal and provincial levels of government in Canada. But, while there is some level of understanding at the federal level in terms of the role and use of evaluation in measuring results, there is significantly less information readily available about the extent to which evaluation is being used at other levels of government. This paper provides a cross-Canada synopsis on the capacity and use of systematic evaluation at the provincial and territorial levels of government. Authors from nine provinces and two territories provide a succinct analysis of the extent to which evaluation is being used in their provincial/territorial government, as well as a description of the challenges and opportunities that lie ahead for evaluation. There is a paucity of published information on this subject, but the paper uses research conducted in 2001 as a benchmark to compare the state of affairs for evaluation within provincial/territorial governments. With limited progress over the past two decades, the paper offers an overview of findings and some proposed actions for the way ahead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".