The Nature of Emergency: The Great Kanto Earthquake and the Crisis of Reason in Late Imperial Japan
Bibliographic record
Abstract
Argument Hijōji(emergency) was an important keyword in the militarist Japan of the 1930s. Previous scholarship has assumed that such language sprung from the global financial crisis of 1929, and subsequent diplomatic events. Our article demonstrates, however, that a full-bodied language of emergency was crafted well before the collapse of the global economy, and against the backdrop of the Great Kanto Earthquake of 1923, which destroyed the Japanese capital. While previous “great earthquakes” had been opportunities to strengthen Japanese participation in the global project of science, this one led more dramatically to a crisis of reason, and indirectly contributed to the spiritual, anti-western, and anti-rational rhetoric of what became the “Showa Restoration.” This and other post-disaster landscapes, we argue, should be examined as compelling sites for the crafting of political language – sites of opportunity and meaning as well as trial. While the phrase “state of emergency” was coined under very different circumstances in post-war Britain, it gained power and charisma in Japan, and likely other places around the world, by its association with natural catastrophe. Thus did modern politics establish a new connection with the traditional realm of the sublime, and in the case of Japan, the supernatural. Emergency's ability to associate politics with nature would never disappear, and has perhaps even strengthened in the early twenty-first century.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".