Enhancing the effectiveness of policy‐relevant integrative research in rural areas
Bibliographic record
Abstract
There has been much debate about the importance of policy‐relevant research in geography over the last decade. There has also been an increasing recognition by policymakers of the importance of integrative (interdisciplinary and transdisciplinary) approaches to policy‐relevant research. However, geographers have been more reluctant than their colleagues in other social and natural sciences to embrace integrative research collaborations. For integrative research to achieve its full potential and to encourage greater participation from the geographical research community, we need to increase our understanding of its potential value, but also some of the challenges that it poses, and how these can be overcome. In this paper, we consider the processes involved in conducting successful integrative research from the perspective of researchers involved in these projects. We base our analysis on the results of a questionnaire survey of international integrative research programmes on environmental issues in rural areas, combined with our own experiences of working in integrative research. We conclude that effective integrative research depends on the establishment of a clear conceptual framework, the use of appropriate temporal and spatial scales in the research, effective language and communication, time and commitment, and trust and respect. We also highlight the value of stakeholder involvement in integrative research to ensure the policy relevance of the work and provide a mechanism to assist with effective knowledge transfer of the results.
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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 | 0.383 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".