Necropolitical Assemblages and Cross-Border Ethics in Hiromi Goto’s Darkest Light
Bibliographic record
Abstract
Drawing on Deleuzian-inflected theories of assemblage, together with recent interventions in the field of affect studies, this article examines Hiromi Goto’s novel Darkest Light (2012) in terms of what I refer to as a multitude of necropolitical assemblages . Depicted as deviant and monstrous, the human and non-human beings portrayed in the novel are often deprived of political rights and thus forced to live and die in the social, economic, and cultural borderlands of our public world. The dispersion of temporal, spatial, and other material borders in Darkest Light , however, signals how these vulnerable populations, despite being stripped of biopolitical currency, are capable of activating change. In this essay, I argue that Goto’s novel proposes a cross-border ethic as a strategy to counteract those necropolitical assemblages that govern contemporary societies, while simultaneously advocating for alternative logics of embodiment, affect, and ethical intervention.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.084 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".