Bibliographic record
Abstract
Japan is still a predominantly monolingual nation with very little concern for the well-being and education of bilingual Japanese or the non-Japanese speaking population living in the country. In this paper, I will discuss the problem of a bilingual's identity from the point of view of linguistic proficiency, using the results of several studies conducted on Japanese. Personal Experience I went to the United States at the age of seven, moved to Canada at the age of nine, and finally returned to Japan at the age of thirteen. At the time, there were no ESL or bilingual programs in New York, nor was there a Japanese weekend school. When I returned to Japan, the word kikokushijo (returnee) had not yet been invented. When I first went to New York, I understood no English, and when I returned to Japan six years later, I had forgotten most of my Japanese. I could hardly even write my name in hiragana--let alone in kanji. In the United States and Canada, I did two and a half years of second grade, a half year of third grade, skipped fifth grade; and, back in Japan, I had to do second-year junior high school twice (not much else you could do when you were ranked number one from the bottom, with a one-in-a-hundred chance of getting into senior high school).
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.067 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".