Bibliographic record
Abstract
Abstract Drawing on ideas from Discourse Grammar ( Heine et al. 2013 ), this article examines characteristics of the Japanese reduplicated mimetics, arguing that they are transcategorial, able to function across different planes of grammar, either as mimetic adverbs belonging to Sentence Grammar (SG) or mimetic “theticals” belonging to Thetical Grammar (TG) ( Kaltenböck et al. 2011 : 879). The former expresses a manner of an action, typically occurring in the immediately preverbal position, as inhuwahuwa(to) uku[mim quotfloat] ‘floatlightly’. By contrast the latter is the speaker’s re-enactment of the event, as in the case of “Huwahuwa, shita de tsubuseru toohu gurai no katasa desu.” ‘Fluffy-fluffy,(the baby food should) have softness liketofuthat can be crushed by (your) tongue.’, where the mimetic ‘re-enacts’ the speaker’s mouth feel (fluffiness) when she put the food into her mouth. The particle drop of the reduplicated mimetics is syntactically optional in SG, but obligatory in TG. The article suggests adding mimetics to the list of theticals, as the fronted zero-marked mimetics followed by a pause display three of the defining prototypical properties of theticals ( Kaltenböck et al. 2011 ): (i) prosodic property (they display comma intonation), (ii) syntactic independence (they are not modifiers of the predicate), and (iii) semantic non-restrictiveness (they do not restrict the semantic content of the predicate).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".