In Search of a Balanced Canadian Federal Evaluation Function: Getting to Relevance
Bibliographic record
Abstract
Abstract: In April 2009, the Treasury Board Secretariat enacted a new Evaluation Policy replacing the previous 2001 version. This new policy has generated much discussion among the evaluation community, including the criticism that it has failed to repair the many shortcomings the function has faced since it centralized in 1977. This article reviews the history of the federal function as to why shortcomings persist and makes two assertions. First, if program evaluation is going to maintain its relevance, it will have to shift its focus from the individual program and services orientation to understanding how these programs and services relate to larger public policy objectives. Second, if program evaluation is to assume a whole-of-government approach, then evidentiary forms must be constructed to serve that purpose. The author makes the argument that evaluation must be far more holistic and calibrative than in the past; this means assessing the relevance, rationale, and effect of public policies. Only in this way can the function both serve a practical managerial purpose and be relevant to senior decision-makers.
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 | 0.183 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.031 | 0.031 |
| Scholarly communication | 0.034 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".