River rhythmicity: A conceptual means of understanding and leveraging the relational values of rivers
Bibliographic record
Abstract
Abstract River rhythmicity refers to the periodic, recurrent phenomena of a riverscape that are synchronized with the rise and fall of river water, creating regimes of river time. River rhythmicity can serve as a lens into the temporal dimension of river formation and socio‐ecological dynamics that are of great interest to many disciplines. In this paper, we introduce river rhythmicity as a conceptual and analytical framework to unify riparian human communities, academic disciplines and water agencies in approaching research and management of rivers. We also explore how the disruptions to riverine rhythms that are experienced by river‐dwelling communities, and are often visible in river discharge data through time, reconfigure, hinder or sever relationships between people and rivers. To ground our discussion in practical, lived experience, we provide brief descriptions of regimes of river time to demonstrate how rhythmic patterns established with rivers in north‐central Canada and Amazonian Colombia shape the lives of two of our co‐authors. By prioritizing holistic accounts of river rhythms, we can elucidate a fuller range of phenomena and their dynamic interactions, revealing riverscape features that are highly valued by local communities yet not often visible to any one discipline. Rhythmicity provides a conceptual framework to help address several challenges facing river conservation and water allocation dilemmas. By emphasizing relationality, it serves to (a) move beyond a biophysical framing of human‐nature connectedness by demonstrating that dynamic processes and relationships are constitutive of rivers, not derivative of them; (b) enhance understanding of how the temporal dimensions of riverine relationships and river dwelling are experienced; (c) highlight the socio‐cultural consequences of changes to river time and (d) centre socially embedded relationships with rivers forged from generations of observations of care and reciprocity. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".