How Did We Get Here? Truth-Listening to Climate Crisis Through Reading Literary Works by Australian First Nations Writers
Bibliographic record
Abstract
Abstract In this paper we theorise climate fiction in the context of Dirrayawadha: Rise Up , by Anita Heiss (2024). Dirrayawadha: Rise Up is a literary novel that narratises historical truths in a dialogic encounter. Through an exploration of love, resilience and resistance, the novel recounts early moments of invasion while simultaneously revealing the links between colonial violence and environmental crisis. We examine four excerpts from the novel to illustrate how the narratisation of historical truths and usage of literary devices and language works. We also show how the translanguaging in the novel, where some sections shift between English and Wiradyuri, enable the text to transcend some of the limitations of English. The novel reveals how the genesis of environmental crisis in so-called Australia begins in the first moments of invasion. Heiss (2022) argues the need for settlers to read more First Nations writing as a form of truth-listening (Kwaymullina, 2020).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".