Bibliographic record
Abstract
In a landmark address to the Australian Academy of the Humanities from 2003, environmental historian Tom \nGriffiths observed that Australia had “a real competitive edge in the ecological humanities.” In his analysis, this \nwas related to “our history as a modern settler society with a long, strong indigenous history, our inheritance \nof a confrontingly different and unique ecology, our inhabitation of an island continent that is also a nation.” \nThis paper proceeds from a reconsideration of how this troubled socio-environmental historical experience \ninformed the emergence and development of the multi- and transdisciplinary project of the eco-humanities in \nAustralia, and proceeds to address a contemporary case study: namely, the contribution of transdisciplinary \nEnvironmental Humanities research to addressing the challenge of living in Australia’s highly fire-prone, and \nfire-adapted, landscapes under the impact of anthropogenic global warming. While challenging the Eurocentric \nimplications of the construction of Australia as a land “down under”, the paper argues that one of the hallmarks \nof the Australian eco-humanities has been its attention to the voices and agency of those who have historically \nbeen silenced, including marginalised human groups (especially Australia’s First Nations), but also nonhuman \nothers and inhuman forces. In this way, Environmental Humanities perspectives from “down under” are \nunsettling the dualisms inherent in what Australian ecophilosopher Val Plumwood termed the “logic of \ncolonisation”, opening a space for creative conversations and collaborations across the divides of disciplines, \ncultures, and even species.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.064 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".