Unveiling assumptions through interdisciplinary scrutiny: Observations from the German Priority Program on Climate Engineering (SPP 1689)
Bibliographic record
Abstract
Abstract The interdisciplinary exchange in climate engineering research offers a unique opportunity to make assumptions more explicit for such research projects. While making assumptions explicit is the standard in all disciplinary sciences, some assumptions in the context of societal challenges can only be usefully unveiled, discussed, and verified from the perspective of other research disciplines. Results from successful interdisciplinary collaborations are then more accessible and more generalizable to actors beyond the confines of the academic community. We aim to illustrate how interdisciplinary exchange helps to unveil assumptions in research endeavors and why this is important for successful interdisciplinary collaborations. We therefore follow different stages of the German Priority Program on Climate Engineering (SPP 1689), which we use as an example case of a successful interdisciplinary project. SPP 1689 focused on risks, challenges, and opportunities of Climate Engineering from the perspectives of numerous disciplines. Major results were that the initial assessments of technologies had to be sobered, the consideration of trade-offs is crucial for the potential assessment, and governance issues appeared larger than previously considered. From the reflections of SPP 1689, we conclude with three lessons learned: (1) The project profited from egalitarian organizational structures and communicative practices, preventing the predominance from single disciplines. (2) Within the project continuous efforts were undertaken to foster interdisciplinary understanding. In addition, the flexible project structure allowed for the accommodation of research needs arising as a result of these exchanges. (3) SPP 1689 offered early career researchers a platform for professional exchange on common challenges and best practices of being a part of an interdisciplinary research project.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".