Convergence research as transdisciplinary knowledge coproduction within cases of effective collaborative governance of social-ecological systems
Bibliographic record
Abstract
Successful collaborative governance (CG) of social-ecological systems (SES) involves multiple stakeholders convening iteratively over the long term to reach a commonly held vision. This often involves building knowledge for social learning processes induced to come to collective decisions about managing complex systems in flux. Because of the complexity of any SES in the Anthropocene, this coproduced knowledge is frequently transdisciplinary, using a convergence of applied and scientific knowledge from a variety of disciplines and stakeholders outside academia. We find evidence that these cases of effective SES CG involve both knowledge coproduction and convergence research. We evaluated seven case studies of CG across four continents using criteria (principles and methods) developed to facilitate and describe convergence research on SES and found them to be largely present. We also assess these CG cases using indicators of knowledge coproduction, and show that they all involved transdisciplinary knowledge coproduction, which can provide an informative lens for deepening our shared understanding of convergence and its application to complex adaptive systems. All the cases selected for this paper are examples of CG of SES in which research was conducted as part of a collaborative effort to improve the social-ecological conditions in a particular place, and several incorporate various forms of knowledge and ways of knowing. We suggest that these cases demonstrate both convergence research and knowledge coproduction because of the overlap and similarity of these concepts, providing a brief comparison and contrasting of these approaches to addressing sustainability problems collaboratively.
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 | 0.039 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".