Bibliographic record
Abstract
Previous studies on Western loanwords in Japanese showed that they account for approximately 10 percent of the Japanese lexicon and their number is continuing to grow. This thesis examined the functions of Western loanwords in the language of newspaper articles. 3,844 occurrences of loanwords were collected from four consecutive issues of Yomiuri Shinbun Satellite Edition published in April 2001 and classified into four main functional categories. The four main functional categories were "technical terms," "lexical-gap-fillers," "elevating the images of the referents," and "replacing the native vocabulary items." "Technical terms" were terminologies used specifically in certain fields such as sports, politics, medicine, etc., regardless of their familiarity among the general public. "Lexical-gap-fillers" could be divided into two types: those which denoted novel objects or concepts ("true LGFs") and those which filled lexical gaps by having either broader or more specific meanings than native terms ("semi-LGFs"). The loanwords in the "elevating the images of the referents" function could either have stylistic effects or a euphemistic role depending on the connotations of native equivalents. If a loanword replaced the native equivalent with a neutral connotation, it created a better image by adding stylishness, prestige, casualness, etc. On the other hand, if the native term had negative connotation, then the loanword functioned as a euphemism to conceal the a negativity. "Replacing native equivalents" could be divided into three stages according to the degrees of replacement. The loanwords in the earliest stage functioned as synonyms of the native equivalents. The next stage included loanwords that are used more commonly than their native counterparts in everyday language. The loanwords in the most advanced stage have almost completely replaced the native terms, which have become obsolete. The quantitative analysis of the above functions showed that "lexical-gap-fillers" was the most prevalent function (45.08%). This result was contradictory to the result from Takashi's (1990a) quantitative study on functions of English loanwords in advertisement texts, which revealed that the main function was to create better images of referents. Thus, this thesis concluded that the most prominent function of loanwords is different depending on text styles and purposes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".