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Record W4205904115 · doi:10.1002/rra.3907

River conversations: A confluence of lessons and emergence from the Taieri River and the Nechako River

2021· article· en· W4205904115 on OpenAlexafffundabout
Margot W. Parkes

Bibliographic record

VenueRiver Research and Applications · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of Northern British Columbia
FundersCanadian Institutes of Health ResearchHealth Research Council of New ZealandVancouver Foundation
KeywordsConfluenceRiver managementHydrology (agriculture)EstuaryGeologyEnvironmental scienceOceanographyEnvironmental resource managementComputer scienceGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Drawing on ongoing patterns of learning and relationship, this paper offers a reflection and acknowledgement on the notable influence of two rivers and their role as respected and highly valued “eco‐social elders”: The Taieri River in Otago, New Zealand, and the Nechako River in northern British Columbia, Canada. The paper is motivated by the question: “ If a river has ‘voice’, what can be learned from the emergence arising from rivers ‘in conversation’? ”. At the heart of the reflection are the themes of confluence and emergence—ways in which we grasp the coming together of things, especially when that combination is more than the sum of their parts. The paper aims to explore a “conversation” between the river teachings of the Taieri River and the Nechako River, identifying examples of patterns and connections between distinct river “voices,” and how these may contribute to emergence and ongoing conversations among different rivers and their teachings. The paper commences with an introduction to both rivers, identifying points of direct comparison and contrast, then moving to explore themes of confluence, weaving and emergence, combining ecological, metaphorical, and personal perspectives. The conversation then progresses downstream to river–ocean relationships, reflecting on rivers as eco‐social elders that inspire conversations, provide a sense of home, and offer a point of reference to consider the wider influence on rivers and waterways on the health of diverse species within catchments and across the planet.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.008
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 head0.055
GPT teacher head0.371
Teacher spread0.316 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes3
Has abstractyes

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