<i>The Ocean on Fire: Pacific Stories from Nuclear Survivors and Climate Activists</i>. By Anaïs Maurer
Bibliographic record
Abstract
Despite being on the forefront of climate change impact and climate activism as well as the site of over 300 hundred nuclear detonations and the home of transnational nuclear abolitionist movements, all too often, Oceania is ignored, written over, and obscured by those outside it. In celebrating the life-affirming work of Pacific artists and activists, contrastingly, Anaïs Maurer exalts the region’s storied histories, heartbreak, and fortitude. In The Ocean on Fire, Maurer centers Oceania to think about nuclear war and climate collapse as “inextricably linked in the nuclearized Anthropocene” and the necessity to “challenge them both simultaneously” (13). While this book should surely be praised for its extensive analysis across and between nuclearization and climate change in the Pacific, The Ocean on Fire also makes a significant contribution to contemporary Pacific literary history and its highlighting of Indigenous environmental epistemologies of the Pacific. With an impressive multilingual archive in French, Spanish, English, Tahitian, and ‘Uvean, Maurer identifies strategies of resistance uniting the region from 1945 to the present. As Maurer notes, too often and almost exclusively, Pacific literary studies have been contained within their own regions, archipelagoes, imperial contexts, or linguistic disciplines. Maurer’s transnational and multi-linguistic approach offers a new opportunity to appreciate the urgency of contemporary environmental injustices and environmental activisms, as well as how those are longstanding traditions in Oceania. Not only does Maurer show how Oceanians have always understood themselves in genealogical and political relation, she also illustrates how Pacific Islands storytelling has never stopped, despite these three imperially induced apocalypses (epidemiological, nuclear, and climate).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.118 | 0.043 |
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".