Braiding knowledges of braided rivers – the need for place‐based perspectives and lived experience in the science of landscapes
Bibliographic record
Abstract
Abstract The compounding societal and environmental crises of the Anthropocene necessitate a more holistic, critical and systems understanding of our relationship to and with the earth surface. We are now aware that every landscape is touched by man, a mosaic of recorded artifacts of historical human activity. These effects emerge from distinct perspectives that require global, local, and complex frameworks of inquiry. In order to address the braided realities of this age, geomorphologists need to embrace diverse ways of knowing, most especially indigenous, local and place‐based knowledges of landscapes and our role in shaping them. We need to examine how a singular, objective standpoint in the scientific process privileges determinism over other ways of seeing and being. In this commentary, I argue that the discipline of geomorphology as it is commonly practiced in the Global North is ill‐suited to address the crises of the Anthropocene. In order to reorient the discipline towards a more ethical and societally‐relevant role, we need to seek and integrate place‐based, local and situated perspectives into the scientific work of understanding the landscapes we are working in, particularly as so many of the communities most impacted by these changing landscapes are the least involved in guiding our scientific efforts and outcomes.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.118 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".