Science for life: an evaluation of New Zealand's health research investment system based on international benchmarks
Bibliographic record
Abstract
During the past decade there have been major developments in the way that research investments have been monitored and evaluated. While there are differences in the ways governments fund research around the world, and a diversity of approaches to evaluation, there are a number of common themes that can be observed in national experiences. As the importance of evaluation increases, the gap between current practice and best practice becomes more significant, and the need for comparative study and methods development grows. Current international ‘better-practice’ approaches to research evaluation and performance indicators reflect two important considerations. First, they make a clear distinction between input, output and outcome indicators and assessments of impact. Only limited refinements have occurred in recent years in input and output performance indicators. However, quite considerable developments have occurred in relation to the development of indicators and approaches for assessing the outcomes and impact of research.1 Second, evaluation and reporting mechanisms vary considerably according to the intended audience for the reporting. In particular, as nations move toward strategically targeting limited government research resources reporting demands at the programme level, and for specific stakeholder groups becomes all the more pressing.
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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 | 0.234 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".