Climate Crisis and Narrative Repair: Eco-Critical Perspectives in Contemporary Australian Writing
Bibliographic record
Abstract
This abstract examines the role of contemporary Australian literature in addressing the climate crisis through the lens of “narrative repair,” an eco-critical framework focused on healing the broken relationship between humans and the non-human world. It argues that Australian writers, uniquely positioned within a continent experiencing acute climate impacts and possessing deep Indigenous ecological knowledge, are crafting narratives that actively challenge anthropocentrism, colonial legacies of extraction, and the pervasive sense of eco-anxiety. By analyzing key texts – Tony Birch’s The White Girl (2019), Alexis Wright’s The Swan Book (2013), and Richard Flanagan’s Gould’s Book of Fish (2001) – this study explores diverse strategies of narrative repair. Birch’s novel centres Indigenous sovereignty and care for Country as fundamental to climate justice, offering a model of relationality. Wright employs magical realism and cyclical time to disrupt linear, progress-driven narratives that fuel environmental destruction, emphasizing interconnectedness and resilience. Flanagan’s historical fiction exposes the deep roots of ecological exploitation in Tasmania’s colonial past, linking the violence against humans and nature. Collectively, these texts demonstrate how narrative repair operates by: centring marginalized voices and knowledge systems; reconfiguring narrative form and temporality to reflect ecological complexity; fostering empathy for non-human entities; and imagining alternative, sustainable futures grounded in responsibility and reciprocity. This eco-critical analysis reveals contemporary Australian writing as a vital site for cultural and ecological reparation, offering not just critique of the climate crisis but active pathways towards healing and renewed kinship with the living world. DOI - https://doi.org/10.65525/SVUP.9788199392021.2025.44-57.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.025 | 0.038 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| 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".