Knowledge Co-Production and Transdisciplinarity: Opening Pandora’s Box
Bibliographic record
Abstract
This Special Issue aims to reflect on knowledge co-production and transdisciplinarity, exploring the mutual interaction between water governance and water research. We do so with contributions that bring examples from diverse parts of the world: Bolivia, Canada, Germany, Ghana, Namibia, the Netherlands, Palestine, and South Africa. Key insights brought by these contributions include the importance of engaging the actors from early stages of transdisciplinary research, and the need for an in-depth understanding of the diverse needs, competences, and power of actors and the water governance system in which knowledge co-production takes place. Further, several future research directions are identified, such as the examination of knowledge backgrounds according to the individual and collective thought styles of different actors. Together, the eight papers included in this Special Issue constitute a significant step toward a better understanding of knowledge co-production and transdisciplinarity, with a common thread for being reflective and clear about their complexity, and the political implications and risks they pose for inclusive, plural and just water research and governance.
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 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".