Getting personal with collaborative sustainability experimentation: Reflections and recommendations from a transdisciplinary partnership with the Swedish craft beer sector
Bibliographic record
Abstract
This paper provides reflections on transdisciplinary knowledge coproduction and experimentation processes from sustainability researcher perspectives. It centers on a 5-year period of collaborative research with small- and medium-sized enterprises in an Urban Living Lab in the Swedish craft beer sector. Nine reflections cover a variety of issues and potentials encountered during numerous interactions with societal partners, and are structured by three levels: organizational, interpersonal and intrapersonal. Based on the reflections, authors then propose a set of seven considerations and recommendations for how to more effectively collaborate in such transdisciplinary constellations. The recommendations apply across the three levels, and describe an approach to collaborative research that asks the researcher to be open, transparent, self-aware and intentional, reflective and reflexive, and both adaptive and flexible. Furthermore, they aim to create soft structures to facilitate understanding and mutual learning, such as designating "organizational champions", as well as to embed collaborative reflections into recurring meetings with partners to maintain trust and capture sustainability knock-on opportunities as they arise.
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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 | 0.099 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.047 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".