Assessing the Quality of Aboriginal Program Evaluations
Bibliographic record
Abstract
Abstract: Evaluations have gained in popularity in Canada since the 1990s, but statistical data indicate that the resources allocated to this management tool have not increased accordingly, despite the increased demand. During the same period, regardless of significant efforts to optimize governance, the Canadian federal government's management of issues related to Aboriginal peoples presents some weaknesses. Because evaluation may directly affect the administration of public programs, this study proposes a meta-evaluation of First Nations program evaluations. To do so, we replicate a methodology previously used by the Treasury Board Secretariat in 2004 to complete a vast study assessing the quality of evaluation in Canada. This article, based on the systematic analysis of a nonprobability sampling of more than 20 program evaluation reports, has applied the TBS's meta-evaluation techniques to the Aboriginal context. The results show that the evaluation of Aboriginal programs is of good, and even excellent, quality and suggest that the TBS's evaluation policy has had a definitive impact on evaluation quality.
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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 | 0.072 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".