Bibliographic record
Abstract
In spite of large-scale and ever-increasing translation activities in Japan, professional translation is an uncertain, non-lucrative, socially not highly regarded occupation. Most free-lance translators do translation as a secondary activity. The situation may be changing with the growing number of translation schools that have opened in recent years, which may increase the number of professional translators. Most of the free-lance translation work is done through translation agencies in the technical and industrial fields, though young translators aspire to do literary translation. Other translators work for publishers who provide a significant amount of work, since foreign novels and other books are translated in very large numbers. Harlequin and Harlequin-like series also provide translation opportunities through publishers, though their quality is not up to literary standards. A significant part of translation work for publishers is actually done by "shitayaku", "sub-contractors" of the translators, whose names generally do not even appear on the book covers. These shitayaku eventually become full-fledged translators themselves. Japanese translators work mostly in isolation, though some translation school students'groups survive graduation and continue working collectively for a time. Atany rate, the image of the independent translator working little, earning much and enjoying a leisurely life is not quite true. In spite of the difficulties that young translators have to surmount and their rather uncertain professional and financial prospects, translation has a significant cultural role to play in Japanese society.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.021 |
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".