River co-learning arenas: principles and practices for transdisciplinary knowledge co-creation and multi-scalar (inter)action
Bibliographic record
Abstract
This paper develops the methodological concept of river co-learning arenas (RCAs) and explores their potential to strengthen innovative grassroots river initiatives, enliven river commons, regenerate river ecologies, and foster greater socio-ecological justice. The integrity of river systems has been threatened in profound ways over the last century. Pollution, damming, canalisation, and water grabbing are some examples of pressures threatening the entwined lifeworlds of human and non-human communities that depend on riverine systems. Finding ways to reverse the trends of environmental degradation demands complex spatial-temporal, political, and institutional articulations across different levels of governance (from local to global) and among a plurality of actors who operate from diverse spheres of knowledge and systems of practice, and who have distinct capacities to affect decision-making. In this context, grassroots river initiatives worldwide use new multi-actor and multi-level dialogue arenas to develop proposals for river regeneration and promote social-ecological justice in opposition to dominant technocratic-hydraulic development strategies. This paper conceptualises these spaces of dialogue and action as RCAs and critically reflects on ways of organising and supporting RCAs while facilitating their cross-fertilisation in transdisciplinary practice. By integrating studies, debates, and theories from diverse disciplines, we generate multi-faceted insights and present cornerstones for the engagement with and/or enaction of RCAs. This encompasses five main themes central to RCAs: (1) River knowledge encounters and truth regimes, (2) transgressive co-learning, (3) confrontation and collaboration dynamics, (4) ongoing reflexivity, (5) transcultural knowledge assemblages and translocal bridging of rooted knowledge.
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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 | 0.040 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.092 |
| Scholarly communication | 0.031 | 0.028 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".