Bibliographic record
Abstract
Geomorphology offers an effective entry point into wider debates across geography and the sciences, framing understandings of landscapes as manifestations of complex and emergent relationships that can be used as a platform to support conversations among multiple and diverse worldviews. Physical geographers have much to contribute in moving beyond monological (one only) views of landscapes. This paper draws upon concepts of emergence, connectivity and space-time relationality to develop an ‘ethnogeomorphic’ outlook upon biophysical-and-cultural (‘living’) landscapes. This perspective is grounded through ethnographic case studies with Indigenous 1 communities in Australia and Canada that examine knowledge production and concerns for environmental negotiation and decision-making. Extending beyond a traditional approach to ethnosciences, ethnogeomorphology seeks to move beyond cross-disciplinary scientific disciplines (and their associated epistemologies) towards a shared (if contested) platform of knowledge transfer and communication that reflects multiple ways of connecting to landscapes. Convergent perspectives upon landscape understandings are highlighted from Indigenous knowledges and emerging, relational approaches to geomorphic analysis. Ethnogeomorphology presents a situated, non-relativist response to people–landscape connections that reflects and advocates sentient relationships to place. Potential applications of ethnogeomorphology as an integrating theme of geographic inquiry are explored, highlighting important tensions in the knowledge production process.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".