Measurement of S&T Performance in the Government of Canada: From Outputs to Outcomes
Bibliographic record
Abstract
A major trend in the assessment of R&D program performance over recent years has been the shift from a focus on activities and outputs as measures of success to a more comprehensive perspective which includes analysis of the recipients and beneficiaries and the immediate, intermediate, and longer term outcomes of R&D. This transition to a more complete view of the performance has proven more difficult in practice than in theory, as it involves a significant culture shift. This article describes how some groups in the Govenrnment of Canada have used a performance framework approach to successfully measure R&D outcomes performance in federal organizations. The Canadian experience suggests that three elements are critical to the successful establishment of a performance management culture in an organization. First, a shared vision of the role performance information can play in the management process is necessary. Second, there must be a commitment to the vision as demonstrated by the appropriate organizational incentives and culture - including senior management support. Finally, the organizatian must have the capacity not only to produce credible performance information, but also to use it effectively.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".