L’examen de la qualité des évaluations fédérales: une méta-évaluation réussie?
Bibliographic record
Abstract
Abstract: Evaluation quality is a fundamental issue within the evidence-based management and policy movement. The Treasury Board Secretariat of Canada (TBS) conducted a wide-ranging review of the quality of federal evaluations in 2004. In terms of its relevance and methodology and the credibility of its conclusions, is the meta-evaluation a success? This article answers the question by presenting an evaluation of the TBS quality review. Despite serious shortcomings with respect to the quality theory and criteria, design, coding process, and data analysis, the meta-evaluation conclusions are relevant and credible overall. Lessons learned from this quality review are proposed as recommendations for evaluators and officials responsible for evaluation units who wish to conduct a similar review that does not suffer from the same shortcomings.
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 | 0.675 | 0.851 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.019 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".