Place-based knowledge transfer in a local-to-global and knowledge-to-action context: key steps and facilitative factors
Bibliographic record
Abstract
Rapid global change threatens to outstrip global efforts to establish sustainable stewardship of social-ecological systems (SES). Place-based research can enhance effectiveness of global sustainability policies and actions by providing contextualized knowledge underpinning bottom-up solutions. However, the use and transfer of place-based knowledge remains a major challenge. In this study, we analyze place-based knowledge transfer in a local-to-global and knowledge-to-action context. We aim to provide insights on when, how, and why place-based research can inform decision making at the global scale and lead to action toward more sustainable and just futures. Our iterative and exploratory methodology involved alternating rounds of literature reviews and interviews with interdisciplinary researchers. We identified four key steps (place-based knowledge production, knowledge synthesis, knowledge use at the global scale, and knowledge revision and lessons learned) and five facilitative factors (bridging organizations, knowledge brokers, boundary organizations, institutionalized knowledge governance, and polycentric governance systems), which provide a comprehensive understanding of place-based knowledge transfer. Our conceptual framework provides suggestions on how to set up place-based knowledge transfer to be more effective, complete, and inclusive. Furthermore, our study discusses two major structural challenges that currently inhibit place-based knowledge transfer and shows ways forward for science and policy to overcome these. We argue that place-based knowledge transfer can be an effective means to undo dominant power relations and the epistemic status quo and enable a shift from short-termism in science and policy toward more long-term SES goals. Therefore, it is seminal to open up the predominant value system to more diverse knowledge systems, signifying a shift away from global decision making that is guided by neoliberal capitalist principles and over-emphasizes short-term and individual gains. Finally, it is crucial to prioritize learning over knowing to exploit the long-term value of place-based knowledge transfer.
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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 | 0.040 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.018 | 0.034 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".