The "Usability" of Evaluation Reports: A Precursor to Evaluation Use in Government Organizations
Bibliographic record
Abstract
Background: According to the Treasury Board of Canada’s Policy on Evaluation (2009), evaluations produced by federal government departments must contribute to decision-making at an organizational level (mainly summative) as well as a program level (mainly formative). Previous research shows that although the formative objectives of evaluation are generally reached, the use of evaluation for broader, budgetary management is limited. However, little research has been conducted thus far on this issue. Purpose: This study investigates the extent to which program evaluation is used in the Canadian federal government for budgetary management purposes. Setting: This paper outlines the results obtained following the first component of a two-pronged research strategy focusing on evaluation use in Canadian federal government organizations. Intervention: N/A Research Design: Two federal agencies were recruited to participate in organizational case studies aiming to identify the factors that facilitate the use of evaluation for budgetary reallocation exercises. Data Collection and Analysis: This report presents the findings from a detailed analysis of evaluation reports published by both agencies between 2010-2013. The data were collected from public evaluation reports and analyzed using NVivo. Findings: The preliminary findings of the study show that instrumental use has occurred or can be expected to occur, based on the types of recommendations outlined in the reports reviewed and on the responses to the evaluations produced by program managers.
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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 | 0.379 | 0.607 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".