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Record W4404521604 · doi:10.1080/13549839.2024.2428215

River co-learning arenas: principles and practices for transdisciplinary knowledge co-creation and multi-scalar (inter)action

2024· article· en· W4404521604 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLocal Environment · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of British Columbia
FundersEuropean Commission
KeywordsGrassrootsReflexivitySociologyCommonsParticipatory action researchCorporate governancePoliticsEnvironmental ethicsPolitical scienceSocial scienceManagement

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.374
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it