Bibliographic record
Abstract
To the Editor: Thank you for including Subverting Exclusion: Transpacific Encounters with Race, Caste, and Borders, 1885–1928 (2011) among the books reviewed in your March 2013 issue. Unfortunately, your reviewer's criticism of my book is centered on my purported failure to sustain an argument I do not make. Professor Paul Spickard states in his review that I argue that “a large portion of the Japanese immigrant population were burakumin or other hereditary outcastes.” (JAH, March 2013, p. 1269). In fact, I explain in the opening chapter that “the stigma associated with outcaste status even today has made it impossible to arrive at any concrete estimate of the number of buraku jūmin who emigrated to the United States and Canada during the late nineteenth and twentieth centuries” (Subverting Exclusion, p. 32). My purpose in this section is to explain why I do not attempt to quantify this migration, not to make any specific claim as to the number of buraku jūmin who emigrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.070 | 0.042 |
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".