A Time of Crisis: Japan, the Great Depression and Rural Revitalization . By Kerry Smith. Cambridge, MA: Harvard University Asia Center, distributed by Harvard University Press, 2001. Pp. xvii, 481. $40.00.
Bibliographic record
Abstract
“Rural crisis” is a concept with which anyone who has lived through the summer of 2001 in Britain is only too familiar; the parallels between the rhetoric employed daily in the media here, and that surrounding the undoubtedly much more severe crisis that hit Japanese farm households in the 1930s, are a reminder that periodic agricultural instability remains an as-yet unsolved problem in industrial economies. Nonetheless, Kerry Smith's study of the impact of the Great Depression on rural Japan, which interweaves accounts of the national-level policymaking process with a case study of one village, locates Japan's rural crisis within the specific process of economic and political change that generated both the wartime political economy and significant features of the postwar institutional structure. In this, he reflects the ongoing work of a number of Japanese scholars who are seeking to locate the grass-roots-level continuities between the immediate prewar and wartime periods and the postwar era of reform and growth. However, his wide-ranging study is the first in English to trace in such detail, through the experiences of an individual village and its inhabitants, the impact of and responses to the depression in rural Japan.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".