Bibliographic record
Abstract
For five years after his return from his travels, Fukuzawa mainly worked as a teacher and translator in Edo. This was a time of growing tension and threat to the old order. For instance, on one occasion he moved out of the city fearing a British attack. Because of antiwestern sentiments, he avoided certain contacts. But his family grew and he settled into writing. In 1866 he published the first volume of Seiyo Jijo, Things Western , which sold, in the end, over a quarter of a million copies. He was made a retainer of the Shogun and continued to work as a teacher. Then in January 1867 he went on his third and last overseas expedition, again to America, and returned six months later. Although he comments less on what he learnt from direct observation, he came back with other treasures. On my second journey to America, I had received a much larger allowance than on the previous one. With all my expenses being paid by the government, I was able to purchase a good number of books. I bought many dictionaries of different kinds, texts in geography, history, law, economics, mathematics, and every sort I could secure. They were for the most part the first copies to be brought to Japan, and now with this large library I was able to let each of my students use the originals to study. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".