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Record W4224065402 · doi:10.1002/esp.5380

Braiding knowledges of braided rivers – the need for place‐based perspectives and lived experience in the science of landscapes

2022· article· en· W4224065402 on OpenAlexaff
Michèle Koppes

Bibliographic record

VenueEarth Surface Processes and Landforms · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnthropoceneSituatedEnvironmental ethicsSociologyIndigenousEpistemologyEngineering ethicsEcologyComputer scienceEngineeringPhilosophy

Abstract

fetched live from OpenAlex

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.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.998

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 studies0.0030.000
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 head0.054
GPT teacher head0.361
Teacher spread0.307 · 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 designQualitative
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

Citations20
Published2022
Admission routes1
Has abstractyes

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