Joint knowledge production for climate change adaptation: what is in it for science?
Bibliographic record
Abstract
Both in literature and in practice, it is claimed that joint knowledge production (JKP) by researchers, policy makers, and other societal actors is necessary to make science relevant for addressing climate adaptation. Although recent assessments of JKP projects have provided some arguments in favor of their societal merit, much less is known about their scientific merit. We explored the latter by developing a conceptual framework addressing characteristics of doing JKP as well as hypotheses on potential merits and pitfalls in terms of its process, output, and impact for science. Semistructured interviews with six environmental science research leaders as well as discussions with five researchers involved in past JKP projects were used to start operationalizing the framework into criteria and compiling a survey. This survey was filled out by 144 researchers involved in Knowledge for Climate, a large Dutch multiactor research program. The findings suggest that, at least in the context of recently carried out Dutch climate adaptation projects, JKP contributes to a broader empirical knowledge base; more reflexivity on the part of researchers; and more publications for policy makers. We conclude this paper by formulating next research steps, including evaluating what would be a proper balance between more versus less participatory forms of scientific knowledge production.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".