Evaluation of RT&D: from 'prescriptions for justifying' to 'user-oriented guidance for learning'
Bibliographic record
Abstract
The measurement and evaluation of research, technology and development (RT&D) has gone through phases over the past 50 years. Over time, high-level measures such as total expenditures on R&D, overall citations and patent production have given way to more contextualized metrics recognizing the inherent differences in innovation subject areas and the need to show mission achievement. This article shows how recently proposed Canadian Academy of Health Sciences (CAHS) metrics were adapted to help frame a case study conducted by the Canadian Cancer Society Research Institute (CCSRI). Early results suggest that the framework provides a useful structure to display both a hierarchy of results focused on mission goals, and to build an attributable RT&D and innovation story over time. With this work and other recent developments, evaluation appears poised to go beyond retrospective justification and to become a fully legitimate part of strategic learning for RT&D initiatives.
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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 | 0.362 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.040 | 0.020 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".