Effective mission-oriented research: A new framework for systemic research impact assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.405 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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 it