Bibliographic record
Abstract
Analyzing Toshiko Tamura (Sato)ʼs “Bubetsu (Scorn)”, this paper explores how second generation of Japanese immigrants, the so-called Nisei, conceived of the 1932 Los Angeles Olympic Games. In the two years between her return to Japan from Canada in 1936 and her departure for Shanghai in 1938, Toshiko wrote a number of novels with Nisei protagonists living in the United States. Most works depict the difficulties to live as minority youths during the anti-Japanese sentiment in the United States of the 1920‒30s. “Bubetsu” was the one such novel. Trough an analysis of the representation of Nisei and the Olympic Games in the text, I show that apart from being a sports event, the Games function as powerful device to measure the level of educational and cultural refinement, as well as the spirit of the athletes. Ultimately, they became a scale for measuring excellence of each ethnic group or race, appealing to the audienceʼs “blood pride” through nationalistic enthusiasm.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".