Pragmatic person features in pronominal and clausal speech act phrases
Bibliographic record
Abstract
This paper proposes the necessity of pragmatic person features (Ritter and Wiltschko 2018) in pronominal and clausal speech act phrases in Korean, giving three main arguments for such necessity: (i) pragmatic person [addressee] is needed for hearsay mye which expresses the meaning of you told me without the lexical verb of saying, (ii) pragmatic person [speaker] is needed for the unequal distribution of first-person plural pronouns with exhortative ca ‘let us’, and (iii) pragmatic persons [speaker], and [addressee] are needed for the asymmetric distribution of a dative goal argument in secondhand exhortatives. Based on the compatibility and incompatibility of exhortative ca- and secondhand exhortative ca-mye clauses with a first-person pronoun (e.g., na ‘I’, ce ‘I’, wuli ‘we’, and cehuy ‘we’), I argue that pragmatic person features are needed in syntax to account for their distribution.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".