Toward a Theory of Experimental World Epic: David Mitchell’s Cloud Atlas
Bibliographic record
Abstract
Drawing on world-systems analytic perspectives and development studies, this article argues for the emergence of an experimental world epic during our era of global capitalist transition. As represented by David Mitchell’s Cloud Atlas, among other fictions, this epic demonstrates a radical commitment to global justice through its multi-scalar efforts to reconstitute the histories and horizons of world development, both for the subjects it represents and the global readership it addresses. For Mitchell, an ambivalent aesthetics of global cannibalism serves as a way to encode, critique, and exceed the logic of unfettered global capitalist accumulation, especially as the text self-consciously problematizes its role as a “global cannibal” of world culture and status as a commodity fiction to be consumed in the global literary marketplace. While the aesthetics of cannibalism may be distinctive to Mitchell, this article proposes that the experimental world epic might generally be characterized by its radical commitment to interrogating pivotal moments in world development and global transformation. Such an epic mobilizes world cultural knowledge and global literacies to highlight the deprivations associated with uneven development, enact global cognitive justice, and involve readers as active participants in articulating more ethical horizons for global transformation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".