Real-time and Apparent-time Changes in Semantics : Japanese Classifiers Tested across Generations and after a Quarter Century
Bibliographic record
Abstract
Japanese numeral classifiers are undergoing change (Sanches 1977, Downing 1996, Shimojo 1997).This paper demonstrates the ongoing changes pointed out by previous studies by two types of experimental investigations.In one study we compare generation differences in the use of classifier -ko as reflections of ongoing changes, in an attempt to apply Labov's (1963Labov's ( , 1966) ) apparent-time hypothesis to semantic change, which has rarely been made (Bailey 2004: 319).In another study, we examine how the acceptability ratings of the classifier -hon for various objects have changed in real time by comparing the results of the same experiment conducted with the same speakers in two different periods (in 1987 and 2011) as well as the results from a younger generation in 2011.We argue that 1) the generational differences can be observed in the use of -ko and -hon, which appear to reflect ongoing changes, and 2) the speakers tested a quarter-century ago retain older uses of -hon not favored among present-day younger speakers, but have acquired some new uses, providing caution in treating generation differences as a reflection of semantic changes.* This is a revised version of the paper presented at the Linguistic Society of Japan meeting in 2011.We would like to thank Junko Hibiya for sharing with the second author the excitement of studying variation and change in progress around the time the first experiment reported here was conducted.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".