Effective mission-oriented research: A new framework for systemic research impact assessment
Bibliographic record
Abstract
Abstract Mission-oriented research combines a wide array of natural and social science disciplines to offer solutions for complex and multi-dimensional challenges such as climate change, loss of biodiversity, and scarcity of natural resources. The utilization of the outputs of mission-oriented research aims for changes in behavior, policy and practice resulting in real world impacts. Systematically assessing such research impacts and impact-generating processes is novel and offers great potential to plan for impactful research. This article develops a framework for systemic research impact assessment (RIA) on the basis of a literature review taking natural resource management (NRM) research as an example. The review compiles and analyzes 70 relevant RIA approaches. The resulting framework combines four components for improving societal impacts (1) an integrated component enabling reflection of impacts on all sustainability dimensions, (2) a missions component orienting toward societal goals to ensure societal relevance, (3) an inclusive component enabling wide participation to ensure legitimacy of research and its impact, and (4) a strategic component to choose appropriate assessment scales and time dimensions to ensure effectiveness. We provide suitable examples for the framework and we conclude with a call for an increased use of systemic and formative RIA that incorporate participatory strategies for research priority setting as well as socially deliberated target systems (e.g. SDGs), to plan for impactful mission-oriented research.
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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 | 0.313 | 0.139 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.027 | 0.013 |
| Science and technology studies | 0.007 | 0.081 |
| Scholarly communication | 0.034 | 0.033 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.007 | 0.011 |
| 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".